Cannabis affects people differently: inter-subject variation in the psychotogenic effects of Δ9-tetrahydrocannabinol: a functional magnetic resonance imaging study with healthy volunteers

Background Cannabis can induce transient psychotic symptoms, but not all users experience these adverse effects. We compared the neural response to Δ9-tetrahydrocannabinol (THC) in healthy volunteers in whom the drug did or did not induce acute psychotic symptoms. Method In a double-blind, placebo-controlled, pseudorandomized design, 21 healthy men with minimal experience of cannabis were given either 10mg THC or placebo, orally. Behavioural and functional magnetic resonance imaging measures were then recorded whilst they performed a go/no-go task. Results The sample was subdivided on the basis of the Positive and Negative Syndrome Scale positive score following administration of THC into transiently psychotic (TP; n=11) and non-psychotic (NP; n=10) groups. During the THC condition, TP subjects made more frequent inhibition errors than the NP group and showed differential activation relative to the NP group in the left parahippocampal gyrus, the left and right middle temporal gyri and in the right cerebellum. In these regions, THC had opposite effects on activation relative to placebo in the two groups. The TP group also showed less activation than the NP group in the right middle temporal gyrus and cerebellum, independent of the effects of THC. Conclusions In this first demonstration of inter-subject variability in sensitivity to the psychotogenic effects of THC, we found that the presence of acute psychotic symptoms was associated with a differential effect of THC on activation in the ventral and medial temporal cortex and cerebellum, suggesting that these regions mediate the effects of the drug on psychotic symptom

Methods to study splicing from high-throughput RNA Sequencing data

The development of novel high-throughput sequencing (HTS) methods for RNA (RNA-Seq) has provided a very powerful mean to study splicing under multiple conditions at unprecedented depth. However, the complexity of the information to be analyzed has turned this into a challenging task. In the last few years, a plethora of tools have been developed, allowing researchers to process RNA-Seq data to study the expression of isoforms and splicing events, and their relative changes under different conditions. We provide an overview of the methods available to study splicing from short RNA-Seq data. We group the methods according to the different questions they address: 1) Assignment of the sequencing reads to their likely gene of origin. This is addressed by methods that map reads to the genome and/or to the available gene annotations. 2) Recovering the sequence of splicing events and isoforms. This is addressed by transcript reconstruction and de novo assembly methods. 3) Quantification of events and isoforms. Either after reconstructing transcripts or using an annotation, many methods estimate the expression level or the relative usage of isoforms and/or events. 4) Providing an isoform or event view of differential splicing or expression. These include methods that compare relative event/isoform abundance or isoform expression across two or more conditions. 5) Visualizing splicing regulation. Various tools facilitate the visualization of the RNA-Seq data in the context of alternative splicing. In this review, we do not describe the specific mathematical models behind each method. Our aim is rather to provide an overview that could serve as an entry point for users who need to decide on a suitable tool for a specific analysis. We also attempt to propose a classification of the tools according to the operations they do, to facilitate the comparison and choice of methods.Comment: 31 pages, 1 figure, 9 tables. Small corrections adde

Accelerated Brain Aging in Schizophrenia and Beyond: A Neuroanatomical Marker of Psychiatric Disorders

Structural brain abnormalities are central to schizophrenia (SZ), but it remains unknown whether they are linked to dysmaturational processes crossing diagnostic boundaries, aggravating across disease stages, and driving the neurodiagnostic signature of the illness. Therefore, we investigated whether patients with SZ (N = 141), major depression (MD; N = 104), borderline personality disorder (BPD; N = 57), and individuals in at-risk mental states for psychosis (ARMS; N = 89) deviated from the trajectory of normal brain maturation. This deviation was measured as difference between chronological and the neuroanatomical age (brain age gap estimation [BrainAGE]). Neuroanatomical age was determined by a machine learning system trained to individually estimate age from the structural magnetic resonance imagings of 800 healthy controls. Group-level analyses showed that BrainAGE was highest in SZ (+5.5 y) group, followed by MD (+4.0), BPD (+3.1), and the ARMS (+1.7) groups. Earlier disease onset in MD and BPD groups correlated with more pronounced BrainAGE, reaching effect sizes of the SZ group. Second, BrainAGE increased across at-risk, recent onset, and recurrent states of SZ. Finally, BrainAGE predicted both patient status as well as negative and disorganized symptoms. These findings suggest that an individually quantifiable "accelerated aging" effect may particularly impact on the neuroanatomical signature of SZ but may extend also to other mental disorders

Vorhersage von Psychosen durch stufenweise Mehrebenenabklärung - Das Basel FePsy (Früherkennung von Psychosen)-Projekt

Hintergrund: In Basel haben wir verschiedene Studien zur Verbesserung der Methodik zur Früherkennung von Psychosen (FePsy) durchgeführt. Methodik: Vom 01.03.2000 bis 29.02.2004 wurden 234 Individuen mithilfe des Basler Screening Instruments für Psychosen (BSIP) gescreent. Bei 10 6 Patienten konnte ein Risikostatus für Psychosen diagnostiziert werden, 53 davon konnten bis zu 7 Jahre (Mittel 5. 4 Jahre) nachuntersucht werden. Die weiteren Untersuchungen erfolgten u.a. mit einem spezifisch entwickelten Anamnese - Instrument, verschiedenen Skalen zur Psychopathologie, Untersuchungen der Neuropsychologie u n d Feinmotorik , klinische m und quantitative m EEG, MRI des Gehirns, Labor. Ergebnisse: Allein auf der Basis des BSIP konnte eine relativ zuverlässige Vorher sage getroffen werden: 21 (39.6 % ) der als „ Risikopatienten “Identifizierten entwickelten innerhalb der Beobachtungszeit tatsächlich eine Psychose. Post hoc konnte durch spezifischere Gewichtung der Psychopathologie und Einbezug neuropsychologischer Untersuchungen die Vorhersagegenauigkeit auf 81 % gesteigert werden. Die anderen oben genannten Verfahren können offensichtlich zur weiteren Verbesserung der Prädiktion beitragen. Schlussfolgerungen: Die Risikoabklärung für Psychose sollte stufenweise und unter Einbezug verschiedener Untersuchungsebenen erfolgen. Background: We have conducted various studies in Basel with the aim of improving the methods for the early detection of psychosis (Fruherkennung von Psychosen, FePsy).Methods: From 1.3.2000 to 29.2.2004 234 individuals were screened using the Basel Screening Instrument for Psychosis (BSIP). 106 patients were identified as at risk for psychosis; out of these 53 remained in follow-up for up to 7 years (mean 5.4 years). The assessments were done with a specifically developed instrument for history taking, various scales for the psychopathology, assessments of neuropsychology and fine motor functioning, clinical and quantitative EEG, MRI of the brain, laboratory etc.Results: Based on the BSIP alone, a relatively reliable prediction was possible: 21 (39.6 %) of the individuals identified as at risk developed psychosis within the follow-up time. Post-hoc prediction could be improved to 81 % by weighting psychopathology and including neuropsychology. Including the other domains obviously allows further improvements of prediction.Conclusions: The risk for psychosis should be assessed in a stepwise procedure. In a first step, a clinically oriented screening should be conducted. If an at-risk status is found, further assessments in various domains should be done in a specialised centre

Detection of gene orthology from gene co-expression and protein interaction networks

Background Ortholog detection methods present a powerful approach for finding genes that participate in similar biological processes across different organisms, extending our understanding of interactions between genes across different pathways, and understanding the evolution of gene families. Results We exploit features derived from the alignment of protein-protein interaction networks and gene-coexpression networks to reconstruct KEGG orthologs for Drosophila melanogaster, Saccharomyces cerevisiae, Mus musculus and Homo sapiens protein-protein interaction networks extracted from the DIP repository and Mus musculus and Homo sapiens and Sus scrofa gene coexpression networks extracted from NCBI\u27s Gene Expression Omnibus using the decision tree, Naive-Bayes and Support Vector Machine classification algorithms. Conclusions The performance of our classifiers in reconstructing KEGG orthologs is compared against a basic reciprocal BLAST hit approach. We provide implementations of the resulting algorithms as part of BiNA, an open source biomolecular network alignment toolkit

A quantitative approach to study indirect effects among disease proteins in the human protein interaction network

<p>Abstract</p> <p>Background</p> <p>Systems biology makes it possible to study larger and more intricate systems than before, so it is now possible to look at the molecular basis of several diseases in parallel. Analyzing the interaction network of proteins in the cell can be the key to understand how complex processes lead to diseases. Novel tools in network analysis provide the possibility to quantify the key interacting proteins in large networks as well as proteins that connect them. Here we suggest a new method to study the relationships between topology and functionality of the protein-protein interaction network, by identifying key mediator proteins possibly maintaining indirect relationships among proteins causing various diseases.</p> <p>Results</p> <p>Based on the i2d and OMIM databases, we have constructed (i) a network of proteins causing five selected diseases (DP, disease proteins) plus their interacting partners (IP, non-disease proteins), the DPIP network and (ii) a protein network showing only these IPs and their interactions, the IP network. The five investigated diseases were (1) various cancers, (2) heart diseases, (3) obesity, (4) diabetes and (5) autism. We have quantified the number and strength of IP-mediated indirect effects between the five groups of disease proteins and hypothetically identified the most important mediator proteins linking heart disease to obesity or diabetes in the IP network. The results present the relationship between mediator role and centrality, as well as between mediator role and functional properties of these proteins.</p> <p>Conclusions</p> <p>We show that a protein which plays an important indirect mediator role between two diseases is not necessarily a hub in the PPI network. This may suggest that, even if hub proteins and disease proteins are trivially of great interest, mediators may also deserve more attention, especially if disease-disease associations are to be understood. Identifying the hubs may not be sufficient to understand particular pathways. We have found that the mediators between heart diseases and obesity, as well as heart diseases and diabetes are of relatively high functional importance in the cell. The mediator proteins suggested here should be experimentally tested as products of hypothetical disease-related proteins.</p

Reply to: New Meta- and Mega-analyses of Magnetic Resonance Imaging Findings in Schizophrenia: Do They Really Increase Our Knowledge About the Nature of the Disease Process?

This work was supported by National Institute of Biomedical Imaging and Bioengineering Grant No. U54EB020403 (to the ENIGMA consortium)

Exploring Predictors of Outcome in the Psychosis Prodrome: Implications for Early Identification and Intervention

Functional disability is a key component of many psychiatric illnesses, particularly schizophrenia. Impairments in social and role functioning are linked to cognitive deficits, a core feature of psychosis. Retrospective analyses demonstrate that substantial functional decline precedes the onset of psychosis. Recent investigations reveal that individuals at clinical-high-risk (CHR) for psychosis show impairments in social relationships, work/school functioning and daily living skills. CHR youth also demonstrate a pattern of impairment across a range of cognitive domains, including social cognition, which is qualitatively similar to that of individuals with schizophrenia. While many studies have sought to elucidate predictors of clinical deterioration, specifically the development of schizophrenia, in such CHR samples, few have investigated factors relevant to psychosocial outcome. This review integrates recent findings regarding cognitive and social-cognitive predictors of outcome in CHR individuals, and proposes potential directions for future research that will contribute to targeted interventions and improved outcome for at-risk youth

Support Vector Machines and Kernels for Computational Biology

ISSN:1553-734XISSN:1553-735

At risk or not at risk? A meta-analysis of the prognostic accuracy of psychometric interviews for psychosis prediction

An accurate detection of individuals at clinical high risk (CHR) for psychosis is a prerequisite for effective preventive interventions. Several psychometric interviews are available, but their prognostic accuracy is unknown. We conducted a prognostic accuracy meta-analysis of psychometric interviews used to examine referrals to high risk services. The index test was an established CHR psychometric instrument used to identify subjects with and without CHR (CHR+ and CHR−). The reference index was psychosis onset over time in both CHR+ and CHR− subjects. Data were analyzed with MIDAS (STATA13). Area under the curve (AUC), summary receiver operating characteristic curves, quality assessment, likelihood ratios, Fagan’s nomogram and probability modified plots were computed. Eleven independent studies were included, with a total of 2,519 help-seeking, predominately adult subjects (CHR+: N=1,359; CHR−: N=1,160) referred to high risk services. The mean follow-up duration was 38 months. The AUC was excellent (0.90; 95% CI: 0.87-0.93), and comparable to other tests in preventive medicine, suggesting clinical utility in subjects referred to high risk services. Meta-regression analyses revealed an effect for exposure to antipsychotics and no effects for type of instrument, age, gender, follow-up time, sample size, quality assessment, proportion of CHR+ subjects in the total sample. Fagan’s nomogram indicated a low positive predictive value (5.74%) in the general non-help-seeking population. Albeit the clear need to further improve prediction of psychosis, these findings support the use of psychometric prognostic interviews for CHR as clinical tools for an indicated prevention in subjects seeking help at high risk services worldwide