Article

Intermediate phenotypes in psychiatric disorders. Curr Opin Genet Dev

Clinical Brain Disorders Branch, Genes, Cognition, and Psychosis Program, NIMH, NIH, Bethesda, MD, USA.
Current opinion in genetics & development (Impact Factor: 8.57). 03/2011; 21(3):340-8. DOI: 10.1016/j.gde.2011.02.003
Source: PubMed

ABSTRACT The small effect size of most individual risk factors for psychiatric disorders likely reflects biological heterogeneity and diagnostic imprecision, which has encouraged genetic studies of intermediate biological phenotypes that are closer to the molecular effects of risk genes than are the clinical symptoms. Neuroimaging-based intermediate phenotypes have emerged as particularly promising because they map risk associated gene effects onto physiological processes in brain that are altered in patients and in their healthy relatives. Recent evidence using this approach has elucidated discrete, dissociable biological mechanisms of risk genes at the level of neural circuitries, and their related cognitive functions. This approach may greatly contribute to our understanding of the genetics and pathophysiology of psychiatric disorders.

Download full-text

Full-text

Available from: Roberta Rasetti, May 19, 2014
0 Followers
 · 
132 Views
  • Source
    • "Genes do not specify behavior directly but rather encode molecular products that build and govern the functioning of the brain through which behavior is expressed. Such traits may be proximate, such as cognitive functions and behavior intimately tied to survival and reproduction, or distal, such as wealth (adapted from Rasetti and Weinberger 2011) Mark Lett preferences are " constructed " rather than " innate. " However, even constructed preferences do not arise de novo but are influenced by biological factors including genes that encode elements of brain structure and function (Simonson and Sela 2011). "
    [Show abstract] [Hide abstract]
    ABSTRACT: In the first decade of consumer neuroscience, strong progress has been made in understanding how neuroscience can inform consumer decision making. Here, we sketch the development of this discipline and compare it to that of the adjacent field of neuroeconomics. We describe three new frontiers for ongoing progress at both theo-retical and applied levels. First, the field will broaden its boundaries to include genetics and molecular neuroscience, each of which will provide important new insights into individual differences in decision making. Second, recent advances in computational methods will improve the accuracy and out-of-sample generalizability of predicting decisions from brain activity. Third, sophisticated meta-analyses will help consumer neuroscientists to synthesize the growing body of knowledge, providing evidence for consistency and specificity of brain activations and their reliability as measurements of consumer behavior. 1 Consumer neuroscience: the first decade One of the first papers to discuss the relevance of neuroscience and biology to decision research originated from a workshop on the topic at the Invitational Choice Symposium in 2004 (Shiv et al. 2005). The paper asserted that "knowledge in neuroscience can potentially enrich research on decision-making" (p. 375) and "integrating neuroscience with decision-making offers tremendous potential" (p. 385). Ten years later, significant progress has been made in decision neuroscience (broadly used to include decision-making research in neuroeconomics, consumer neuroscience, and social neuroscience). For example, we have achieved a sophisticated understanding of how the brain computes the value of choice options and compares these values leading to choice and how context modulates these basic valuation and decision processes (e.g., Levy and Glimcher 2012). The specific subfield of consumer neuro-science, which applies neuroscience insights and techniques to consumer behavior and
    Marketing Letters 09/2014; 25(3):257-267. DOI:10.1007/s11002-014-9306-1 · 1.06 Impact Factor
  • Source
    • "Genes, while fundamental to vulnerability, are nevertheless difficult to link to the pathoetiological processes that ultimately determine onset of schizophrenia. Hence there is increasing effort to identify proximate imaging endophenotypes of schizophrenia for clinical utility (Meyer-Lindenberg and Weinberger, 2006; Rasetti and Weinberger, 2011) which can be combined with other objective markers and traits to give an extended endophenotype for schizophrenia (Prasad and Keshavan, 2008; Keshavan et al., 2011). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Neurodevelopmental processes are widely believed to underlie schizophrenia. Analysis of brain texture from conventional magnetic resonance imaging (MRI) can detect disturbance in brain cytoarchitecture. We tested the hypothesis that patients with schizophrenia manifest quantitative differences in brain texture that, alongside discrete volumetric changes, may serve as an endophenotypic biomarker. Texture analysis (TA) of grey matter distribution and voxel-based morphometry (VBM) of regional brain volumes were applied to MRI scans of 27 patients with schizophrenia and 24 controls. Texture parameters (uniformity and entropy) were also used as covariates in VBM analyses to test for correspondence with regional brain volume. Linear discriminant analysis tested if texture and volumetric data predicted diagnostic group membership (schizophrenia or control). We found that uniformity and entropy of grey matter differed significantly between individuals with schizophrenia and controls at the fine spatial scale (filter width below 2 mm). Within the schizophrenia group, these texture parameters correlated with volumes of the left hippocampus, right amygdala and cerebellum. The best predictor of diagnostic group membership was the combination of fine texture heterogeneity and left hippocampal size. This study highlights the presence of distributed grey-matter abnormalities in schizophrenia, and their relation to focal structural abnormality of the hippocampus. The conjunction of these features has potential as a neuroimaging endophenotype of schizophrenia.
    Psychiatry Research: Neuroimaging 09/2014; 223(3). DOI:10.1016/j.pscychresns.2014.05.014 · 2.83 Impact Factor
  • Source
    • "Moreover, integrating measures of non-subjective intermediate phenotypes in the symptom trajectory seems to be a promising approach. For example, neuroimaging-based intermediate phenotypes can help us better understand the brain processes behind symptom trajectories [60]. Although it is not possible to scan enough patients with neuroimaging techniques for a GWA study, these results can help us refine our phenotype-measuring system and develop more biology-related categories to work with. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Although there is a wide variety of antidepressants with different mechanisms of action available, the efficacy of treatment is not satisfactory. Genetic factors are presumed to play a role in differences in medication response; however, available evidence is controversial. Even genome-wide association studies failed to identify genes or regions which would consequently influence treatment response. We conducted a literature review in order to uncover possible mechanisms concealing the direct effects of genetic variants, focusing mainly on reports from large-scale studies including STAR*D or GENDEP. We observed that inclusion of environmental factors, gene-environment and gene-gene interactions in the model improves the probability of identifying genetic modulator effects of antidepressant response. It could be difficult to determine which allele of a polymorphism is the risk factor for poor treatment outcome because depending on the acting environmental factors different alleles could be advantageous to improve treatment response. Moreover, genetic variants tend to show better association with certain intermediate phenotypes linked to depression because these are more objective and detectable than traditional treatment outcomes. Thus, detailed modeling of environmental factors and their interactions with different genetic pathways could significantly improve our understanding of antidepressant efficacy. In addition, the complexity of depression itself demands a more comprehensive analysis of symptom trajectories if we are to extract useful information which could be used in the personalization of antidepressant treatment.
    Annals of General Psychiatry 06/2014; 13(1):17. DOI:10.1186/1744-859X-13-17 · 1.53 Impact Factor
Show more