Automated ventricular systems segmentation in brain CT images by combining low-level segmentation and high-level template matching

<p>Abstract</p> <p>Background</p> <p>Accurate analysis of CT brain scans is vital for diagnosis and treatment of Traumatic Brain Injuries (TBI). Automatic processing of these CT brain scans could speed up the decision making process, lower the cost of healthcare, and reduce the chance of human error. In this paper, we focus on automatic processing of CT brain images to segment and identify the ventricular systems. The segmentation of ventricles provides quantitative measures on the changes of ventricles in the brain that form vital diagnosis information.</p> <p>Methods</p> <p>First all CT slices are aligned by detecting the ideal midlines in all images. The initial estimation of the ideal midline of the brain is found based on skull symmetry and then the initial estimate is further refined using detected anatomical features. Then a two-step method is used for ventricle segmentation. First a low-level segmentation on each pixel is applied on the CT images. For this step, both Iterated Conditional Mode (ICM) and Maximum A Posteriori Spatial Probability (MASP) are evaluated and compared. The second step applies template matching algorithm to identify objects in the initial low-level segmentation as ventricles. Experiments for ventricle segmentation are conducted using a relatively large CT dataset containing mild and severe TBI cases.</p> <p>Results</p> <p>Experiments show that the acceptable rate of the ideal midline detection is over 95%. Two measurements are defined to evaluate ventricle recognition results. The first measure is a sensitivity-like measure and the second is a false positive-like measure. For the first measurement, the rate is 100% indicating that all ventricles are identified in all slices. The false positives-like measurement is 8.59%. We also point out the similarities and differences between ICM and MASP algorithms through both mathematically relationships and segmentation results on CT images.</p> <p>Conclusion</p> <p>The experiments show the reliability of the proposed algorithms. The novelty of the proposed method lies in its incorporation of anatomical features for ideal midline detection and the two-step ventricle segmentation method. Our method offers the following improvements over existing approaches: accurate detection of the ideal midline and accurate recognition of ventricles using both anatomical features and spatial templates derived from Magnetic Resonance Images.</p

The Ethics of Delusional Belief

In this paper we address the ethics of adopting delusional beliefs and we apply consequentialist and deontological considerations to the epistemic evaluation of delusions. Delusions are characterised by their epistemic shortcomings and they are often defined as false and irrational beliefs. Despite this, when agents are overwhelmed by negative emotions due to the effects of trauma or previous adversities, or when they are subject to anxiety and stress as a result of hypersalient experience, the adoption of a delusional belief can prevent a serious epistemic harm from occurring. For instance, delusions can allow agents to remain in touch with their environment overcoming the disruptive effect of negative emotions and anxiety. Moreover, agents are not blameworthy for adopting their delusions if their ability to believe otherwise is compromised. There is evidence suggesting that no evidence-related action that would counterfactually lead them to believe otherwise is typically available to them. The lack of ability to believe otherwise, together with some other conditions, implies that the agents are not blameworthy for their delusions. The examination of the epistemic status of delusions prompts us to acknowledge the complexity and contextual nature of epistemic evaluation, establish connections between consequentialist and deontological frameworks in epistemology, and introduce the notion of epistemic innocence into the vocabulary of epistemic evaluatio

Transient activation of dopaminergic neurons during development modulates visual responsiveness, locomotion and brain activity in a dopamine ontogeny model of schizophrenia

It has been observed that certain developmental environmental risk factors for schizophrenia when modeled in rodents alter the trajectory of dopaminergic development, leading to persistent behavioural changes in adults. This has recently been articulated as the "dopamine ontogeny hypothesis of schizophrenia". To test one aspect of this hypothesis, namely that transient dopaminergic effects during development modulate attention-like behavior and arousal in adults, we turned to a small-brain model, Drosophila melanogaster. By applying genetic tools allowing transient activation or silencing of dopaminergic neurons in the fly brain, we investigated whether a critical window exists during development when altered dopamine (DA) activity levels could lead to impairments in arousal states in adult animals. We found that increased activity in dopaminergic neurons in later stages of development significantly increased visual responsiveness and locomotion, especially in adult males. This misallocation of visual salience and hyperactivity mimicked the effect of acute methamphetamine feeding to adult flies, suggesting up-regulated DA signaling could result from developmental manipulations. Finally, brain recordings revealed significantly reduced gamma-band activity in adult animals exposed to the transient developmental insult. Together, these data support the idea that transient alterations in DA signaling during development can permanently alter behavior in adults, and that a reductionist model such as Drosophila can be used to investigate potential mechanisms underlying complex cognitive disorders such as schizophrenia

Delta-9-tetrahydrocannabinol, neural oscillations above 20 Hz and induced acute psychosis

Rationale: An acute challenge with delta-9-tetrahydrocannabinol (THC) can induce psychotic symptoms including delusions. High electroencephalography (EEG) frequencies, above 20 Hz, have previously been implicated in psychosis and schizophrenia. Objectives: The objective of this study is to determine the effect of intravenous THC compared to placebo on high-frequency EEG. Methods: A double-blind cross-over study design was used. In the resting state, the high-beta to low-gamma magnitude (21–45 Hz) was investigated (n=13 pairs+4 THC only). Also, the event-related synchronisation (ERS) of motor-associated high gamma was studied using a self-paced button press task (n=15). Results: In the resting state, there was a significant condition × frequency interaction (p=0.00017), consisting of a shift towards higher frequencies under THC conditions (reduced high beta [21–27 Hz] and increased low gamma [27–45 Hz]). There was also a condition × frequency × location interaction (p=0.006), such that the reduction in 21–27-Hz magnitude tended to be more prominent in anterior regions, whilst posterior areas tended to show greater 27–45-Hz increases. This effect was correlated with positive symptoms, as assessed on the Positive and Negative Syndrome Scale (PANSS) (r=0.429, p=0.042). In the motor task, there was a main effect of THC to increase 65–130-Hz ERS (p=0.035) over contra-lateral sensorimotor areas, which was driven by increased magnitude in the higher, 85–130-Hz band (p=0.02) and not the 65–85-Hz band. Conclusions: The THC-induced shift to faster gamma oscillations may represent an over-activation of the cortex, possibly related to saliency misattribution in the delusional state

Dysfunctional GABAergic inhibition in the prefrontal cortex leading to "psychotic" hyperactivation

<p>Abstract</p> <p>Background</p> <p>The GABAergic system in the brain seems to be dysfunctional in various psychiatric disorders. Many studies have suggested so far that, in schizophrenia patients, GABAergic inhibition is selectively but consistently reduced in the prefrontal cortex (PFC).</p> <p>Results</p> <p>This study used a computational model of the PFC to investigate the dynamics of the PFC circuit with and without chandelier cells and other GABAergic interneurons. The inhibition by GABAergic interneurons other than chandelier cells effectively regulated the PFC activity with rather low or modest levels of dopaminergic neurotransmission. This activity of the PFC is associated with normal cognitive functions and has an inverted-U shaped profile of dopaminergic modulation. In contrast, the chandelier cell-type inhibition affected only the PFC circuit dynamics in hyperdopaminergic conditions. Reduction of chandelier cell-type inhibition resulted in bistable dynamics of the PFC circuit, in which the upper stable state is associated with a hyperactive mode. When both types of inhibition were reduced, this hyperactive mode and the conventional inverted-U mode merged.</p> <p>Conclusion</p> <p>The results of our simulation suggest that, in schizophrenia, a reduction of GABAergic inhibition increases vulnerability to psychosis by (i) producing the hyperactive mode of the PFC with hyperdopaminergic neurotransmission by dysfunctional chandelier cells and (ii) increasing the probability of the transition to the hyperactive mode from the conventional inverted-U mode by dysfunctional GABAergic interneurons.</p

Association between Dopamine Receptor D2 (DRD2) Variations rs6277 and rs1800497 and Cognitive Performance According to Risk Type for Psychosis : A Nested Case Control Study in a Finnish Population Sample

Background There is limited research regarding the association between genes and cognitive intermediate phenotypes in those at risk for psychotic disorders. Methods We measured the association between established psychosis risk variants in dopamine D2 receptor (DRD2) and cognitive performance in individuals at age 23 years and explored if associations between cognition and these variants differed according to the presence of familial or clinical risk for psychosis. The subjects of the Oulu Brain and Mind Study were drawn from the general population-based Northern Finland 1986 Birth Cohort (NFBC 1986). Using linear regression, we compared the associations between cognitive performance and two candidate DRD2 polymorphisms (rs6277 and rs1800497) between subjects having familial (n=61) and clinical (n=45) risk for psychosis and a random sample of participating NFBC 1986 controls (n=74). Cognitive performance was evaluated using a comprehensive battery of tests at follow-up. Results Principal components factor analysis supported a three-factor model for cognitive measures. The minor allele of rs6277 was associated with poorer performance on a verbal factor (p=0.003) but this did not significantly interact with familial or clinical risk for psychosis. The minor allele of rs1800497 was associated with poorer performance on a psychomotor factor (p=0.038), though only in those at familial risk for psychotic disorders (interaction p=0.049). Conclusion The effect of two DRD2 SNPs on cognitive performance may differ according to risk type for psychosis, suggesting that cognitive intermediate phenotypes differ according to the type (familial or clinical) risk for psychosis.Peer reviewe

TARJETA POSTAL NAVIDEÑA [Material gráfico]

ITALIACopia digital. Madrid : Ministerio de Educación, Cultura y Deporte, 201

Advancing schizophrenia drug discovery : optimizing rodent models to bridge the translational gap

Although our knowledge of the pathophysiology of schizophrenia has increased, treatments for this devastating illness remain inadequate. Here, we critically assess rodent models and behavioural end points used in schizophrenia drug discovery and discuss why these have not led to improved treatments. We provide a perspective on how new models, based on recent advances in the understanding of the genetics and neural circuitry underlying schizophrenia, can bridge the translational gap and lead to the development of more effective drugs. We conclude that previous serendipitous approaches should be replaced with rational strategies for drug discovery in integrated preclinical and clinical programmes. Validation of drug targets in disease-based models that are integrated with translationally relevant end point assessments will reduce the current attrition rate in schizophrenia drug discovery and ultimately lead to therapies that tackle the disease process