Explore the vast range of titles available.

Abstract Background and Hypothesis Individuals with schizophrenia have less priors than controls, meaning they rely less upon their prior experiences to interpret the current stimuli. These differences in priors are expected to show as higher alternation rates in bistable perception tasks like the Structure-from-Motion (SfM) paradigm. In this paradigm, continuously moving dots in two dimensions are perceived subjectively as traveling along a three-dimensional sphere, which results in a direction of motion (left or right) that shifts approximately every few seconds. Study Design Here, we tested healthy controls, patients with schizophrenia, siblings of patients with schizophrenia, and patients with depression with both the intermittent and continuous variants of the SfM paradigm. Study Results In the intermittent variant of the SfM paradigm, depressive patients exhibited the lowest alternation rate, followed by unaffected controls. In contrast, patients with schizophrenia and their unaffected siblings displayed significantly higher alternation rates. In the continuous variant of the SfM paradigm, patients with schizophrenia showed the lowest mean percept durations, while there were no differences between the other three groups. Conclusions The intermittent SfM paradigm is a candidate endophenotype for schizophrenia. The aberrant processing in the patients may stem from alterations in adaptation and/or cross-inhibition mechanisms leading to changes in priors, as suggested by current models in the field. The intermittent SfM paradigm is, hence, a trait marker that offers the great opportunity to investigate perceptual abnormalities across the psychiatry spectrum, ranging from depression to psychosis.

More than 20 genetic loci have been associated with risk for Alzheimer’s disease (AD), but reported genome-wide significant loci do not account for all the estimated heritability and provide little information about underlying biological mechanisms. Genetic studies using intermediate quantitative traits such as biomarkers, or endophenotypes, benefit from increased statistical power to identify variants that may not pass the stringent multiple test correction in case–control studies. Endophenotypes also contain additional information helpful for identifying variants and genes associated with other aspects of disease, such as rate of progression or onset, and provide context to interpret the results from genome-wide association studies (GWAS). We conducted GWAS of amyloid beta (Aβ 42 ), tau, and phosphorylated tau (ptau 181 ) levels in cerebrospinal fluid (CSF) from 3146 participants across nine studies to identify novel variants associated with AD. Five genome-wide significant loci (two novel) were associated with ptau 181 , including loci that have also been associated with AD risk or brain-related phenotypes. Two novel loci associated with Aβ 42 near GLIS1 on 1p32.3 ( β  = −0.059, P  = 2.08 × 10 −8 ) and within SERPINB1 on 6p25 ( β  = −0.025, P  = 1.72 × 10 −8 ) were also associated with AD risk ( GLIS1 : OR = 1.105, P  = 3.43 × 10 −2 ), disease progression ( GLIS1 : β  = 0.277, P  = 1.92 × 10 −2 ), and age at onset ( SERPINB1 : β  = 0.043, P  = 4.62 × 10 −3 ). Bioinformatics indicate that the intronic SERPINB1 variant (rs316341) affects expression of SERPINB1 in various tissues, including the hippocampus, suggesting that SERPINB1 influences AD through an Aβ-associated mechanism. Analyses of known AD risk loci suggest CLU and FERMT2 may influence CSF Aβ 42 ( P  = 0.001 and P  = 0.009, respectively) and the INPP5D locus may affect ptau 181 levels (P  = 0.009); larger studies are necessary to verify these results. Together the findings from this study can be used to inform future AD studies.

Major depressive disorder (MDD) is a highly heterogeneous condition in terms of symptom presentation and, likely, underlying pathophysiology. Accordingly, it is possible that only certain individuals with MDD are well-suited to antidepressants. A potentially fruitful approach to parsing this heterogeneity is to focus on promising endophenotypes of depression, such as neuroticism, anhedonia, and cognitive control deficits. Within an 8-week multisite trial of sertraline v. placebo for depressed adults (n = 216), we examined whether the combination of machine learning with a Personalized Advantage Index (PAI) can generate individualized treatment recommendations on the basis of endophenotype profiles coupled with clinical and demographic characteristics. Five pre-treatment variables moderated treatment response. Higher depression severity and neuroticism, older age, less impairment in cognitive control, and being employed were each associated with better outcomes to sertraline than placebo. Across 1000 iterations of a 10-fold cross-validation, the PAI model predicted that 31% of the sample would exhibit a clinically meaningful advantage [post-treatment Hamilton Rating Scale for Depression (HRSD) difference ⩾3] with sertraline relative to placebo. Although there were no overall outcome differences between treatment groups (d = 0.15), those identified as optimally suited to sertraline at pre-treatment had better week 8 HRSD scores if randomized to sertraline (10.7) than placebo (14.7) (d = 0.58). A subset of MDD patients optimally suited to sertraline can be identified on the basis of pre-treatment characteristics. This model must be tested prospectively before it can be used to inform treatment selection. However, findings demonstrate the potential to improve individual outcomes through algorithm-guided treatment recommendations.

IntroductionA growing body of evidence in both chronic and first-episode schizophrenia report increased expression of pro-inflammatory substances in the blood and cerebrospinal fluid of patients. However, there is not much data in the literature on immune alterations in unaffected first-degree relatives (FDRs) of the patients.ObjectivesWe aimed to evaluate inflammatory aberrancies in patients with schizophrenia, their unaffected first-degree relatives (FDRs) and healthy controls.Methods50 chronic, stable schizophrenia patients, 42 FDRs and 40 healthy subjects with no family history (HCSs) were recruited to the study. IL-1β, IL-6, TNF-a and CRP levels were measured. Complete blood counts, fasting glucose and lipid levels were analyzed and neutrofil-lymohocyte ratio (NLR) were calculated.ResultsThere was a significant group difference in all cytokine levels after controlling for age, gender, smoking status, comorbid medical diseases, BMI and blood glucose and tyrigliseride levels (p<.001). FDRs showed significantly higher serum levels of cytokines than HCs, in the same way as the corresponding schizophrenia patients but a lower level. Pairwaise comparisions revealed that the differences were significant between each group after controlling for confounders (p<.001 for all comparisons). However, NLR and CRP levels were not different between groups.ConclusionsOur results support the role of inflammatory aberrancies in the pathophysiology of schizophrenia. The finding of abnormal cytokine levels both in schizophrenic patients and FDRs indicates that such immunological alterations are not exclusive to the patients and can be possible endophenotypes for the disorder.DisclosureNo significant relationships.

Increased neural error-signals have been observed in obsessive-compulsive disorder (OCD), anxiety disorders, and inconsistently in depression. Reduced neural error-signals have been observed in substance use disorders (SUD). Thus, alterations in error-monitoring are proposed as a transdiagnostic endophenotype. To strengthen this notion, data from unaffected individuals with a family history for the respective disorders are needed. The error-related negativity (ERN) as a neural indicator of error-monitoring was measured during a flanker task from 117 OCD patients, 50 unaffected first-degree relatives of OCD patients, and 130 healthy comparison participants. Family history information indicated, that 76 healthy controls were free of a family history for psychopathology, whereas the remaining had first-degree relatives with depression (n = 28), anxiety (n = 27), and/or SUD (n = 27). Increased ERN amplitudes were found in OCD patients and unaffected first-degree relatives of OCD patients. In addition, unaffected first-degree relatives of individuals with anxiety disorders were also characterized by increased ERN amplitudes, whereas relatives of individuals with SUD showed reduced amplitudes. Alterations in neural error-signals in unaffected first-degree relatives with a family history of OCD, anxiety, or SUD support the utility of the ERN as a transdiagnostic endophenotype. Reduced neural error-signals may indicate vulnerability for under-controlled behavior and risk for substance use, whereas a harm- or error-avoidant response style and vulnerability for OCD and anxiety appears to be associated with increased ERN. This adds to findings suggesting a common neurobiological substrate across psychiatric disorders involving the anterior cingulate cortex and deficits in cognitive control.

Functional magnetic resonance imaging of brain responses to biological motion in children with autism spectrum disorder (ASD), unaffected siblings (US) of children with ASD, and typically developing (TD) children has revealed three types of neural signatures: (i) state activity, related to the state of having ASD that characterizes the nature of disruption in brain circuitry; (ii) trait activity, reflecting shared areas of dysfunction in US and children with ASD, thereby providing a promising neuroendophenotype to facilitate efforts to bridge genomic complexity and disorder heterogeneity; and (iii) compensatory activity, unique to US, suggesting a neural system—level mechanism by which US might compensate for an increased genetic risk for developing ASD. The distinct brain responses to biological motion exhibited by TD children and US are striking given the identical behavioral profile of these two groups. These findings offer far-reaching implications for our understanding of the neural systems underlying autism.

Genomic selection - the prediction of breeding values using DNA polymorphisms - is a disruptive method that has widely been adopted by animal and plant breeders to increase productivity. It was recently shown that other sources of molecular variations such as those resulting from transcripts or metabolites could be used to accurately predict complex traits. These endophenotypes have the advantage of capturing the expressed genotypes and consequently the complex regulatory networks that occur in the different layers between the genome and the phenotype. However, obtaining such omics data at very large scales, such as those typically experienced in breeding, remains challenging. As an alternative, we proposed using near-infrared spectroscopy (NIRS) as a high-throughput, low cost and non-destructive tool to indirectly capture endophenotypic variants and compute relationship matrices for predicting complex traits, and coined this new approach ”phenomic selection” (PS). We tested PS on two species of economic interest (Triticum aestivum L. and Populus nigra L.) using NIRS on various tissues (grains, leaves, wood). We showed that one could reach predictions as accurate as with molecular markers, for developmental, tolerance and productivity traits, even in environments radically different from the one in which NIRS were collected. Our work constitutes a proof of concept and provides new perspectives for the breeding community, as PS is theoretically applicable to any organism at low cost and does not require any molecular information.

A rich collection of empirical findings accumulated over the past three decades attests to the diversity of traits that constitute the autism phenotypes. It is unclear whether subsets of these traits share any underlying causality. This lack of a cohesive conceptualization of the disorder has complicated the search for broadly effective therapies, diagnostic markers, and neural/genetic correlates. In this paper, we describe how theoretical considerations and a review of empirical data lead to the hypothesis that some salient aspects of the autism phenotype may be manifestations of an underlying impairment in predictive abilities. With compromised prediction skills, an individual with autism inhabits a seemingly “magical” world wherein events occur unexpectedly and without cause. Immersion in such a capricious environment can prove overwhelming and compromise one’s ability to effectively interact with it. If validated, this hypothesis has the potential of providing unifying insights into multiple aspects of autism, with attendant benefits for improving diagnosis and therapy. Significance Autism is characterized by diverse behavioral traits. Guided by theoretical considerations and empirical data, this paper develops the hypothesis that many of autism's salient traits may be manifestations of an underlying impairment in predictive abilities. This impairment renders an otherwise orderly world to be experienced as a capriciously “magical” one. The hypothesis elucidates the information-processing roots of autism and, thereby, can aid the identification of neural structures likely to be differentially affected. Behavioral and neural measures of prediction might serve as early assays of predictive abilities in infants, and serve as useful tools in intervention design and in monitoring their effectiveness. The hypothesis also points to avenues for further research to determine molecular and circuit-level causal underpinnings of predictive impairments.

Patients with Alzheimer's disease (AD) have abnormally low positron emission tomography (PET) measurements of the cerebral metabolic rate for glucose (CMRgl) in regions of the precuneus and the posterior cingulate, parietotemporal, and frontal cortex. Apolipoprotein E (APOE) ε4 gene dose (i.e., the number of ε4 alleles in a person's APOE genotype) is associated with a higher risk of AD and a younger age at dementia onset. We previously found that cognitively normal late-middle-aged APOE ε4 carriers have abnormally low CMRgl in the same brain regions as patients with probable Alzheimer's dementia. In a PET study of 160 cognitively normal subjects 47-68 years of age, including 36 ε4 homozygotes, 46 heterozygotes, and 78 ε4 noncarriers who were individually matched for their gender, age, and educational level, we now find that ε4 gene dose is correlated with lower CMRgl in each of these brain regions. This study raises the possibility of using PET as a quantitative presymptomatic endophenotype to help evaluate the individual and aggregate effects of putative genetic and nongenetic modifiers of AD risk.

Rationale Autism spectrum disorder (ASD) is a complex heterogeneous neurodevelopmental disorder with onset during early childhood and typically a life-long course. The majority of ASD cases stems from complex, ‘multiple-hit’, oligogenic/polygenic underpinnings involving several loci and possibly gene–environment interactions. These multiple layers of complexity spur interest into the identification of biomarkers able to define biologically homogeneous subgroups, predict autism risk prior to the onset of behavioural abnormalities, aid early diagnoses, predict the developmental trajectory of ASD children, predict response to treatment and identify children at risk for severe adverse reactions to psychoactive drugs. Objectives The present paper reviews (a) similarities and differences between the concepts of ‘biomarker’ and ‘endophenotype’, (b) established biomarkers and endophenotypes in autism research (biochemical, morphological, hormonal, immunological, neurophysiological and neuroanatomical, neuropsychological, behavioural), (c) -omics approaches towards the discovery of novel biomarker panels for ASD, (d) bioresource infrastructures and (e) data management for biomarker research in autism. Results Known biomarkers, such as abnormal blood levels of serotonin, oxytocin, melatonin, immune cytokines and lymphocyte subtypes, multiple neuropsychological, electrophysiological and brain imaging parameters, will eventually merge with novel biomarkers identified using unbiased genomic, epigenomic, transcriptomic, proteomic and metabolomic methods, to generate multimarker panels. Bioresource infrastructures, data management and data analysis using artificial intelligence networks will be instrumental in supporting efforts to identify these biomarker panels. Conclusions Biomarker research has great heuristic potential in targeting autism diagnosis and treatment.