10 research outputs found
Structured randomness: Jamming of soft discs and pins
Simulations are used to find the zero temperature jamming threshold,
, for soft, bidisperse disks in the presence of small fixed particles,
or "pins", arranged in a lattice. The presence of pins leads, as one expects,
to a decrease in . Structural properties of the system near the jamming
threshold are calculated as a function of the pin density. While the
correlation length exponent remains at low pin densities, the
system is mechanically stable with more bonds, yet fewer contacts than the
Maxwell criterion implies in the absence of pins. In addition, as pin density
increases, novel bond orientational order and long-range spatial order appear,
which are correlated with the square symmetry of the pin lattice.Comment: 9 pages, 11 figures, 1 table; This is v2 of an article, revised
thanks to peer revie
Analysis of shared heritability in common disorders of the brain
ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders
Jammed Solids Held Together With Pins: The Effect Of Pin Geometry On Structure And Mechanical Response
Currently, much is known about the theory and broad applicability of the jamming transition. Here, we address unanswered questions on the geometrical role that a scaffolding of fixed particles, or pins , plays in structure and dynamical response of jammed, soft bi- or polydisperse particles. Our 2d system consists of particles and tiny pins which harmonically repel overlaps, fixed in various geometrical arrangements: square, triangular, or honeycomb lattices, or distributed randomly. While at low pin densities the jamming threshold, φj, decreases linearly with pin density, independently of pin geometry; at higher densities it reflects lattice-specific constraints on particle packing, and φj may even increase with pin density. The distribution of bond angles may be anisotropic, and contact force distribution reflect the presence of pins. Changes in the linear elastic response can be seen in bulk and shear moduli, their scaling with pressure near jamming, and a Zener ratio indicating that pin geometry might break the mechanical isotropy of the jammed state
Intensive residential treatment for severe obsessive-compulsive disorder: Characterizing treatment course and predictors of response
Reappraisal of spontaneous stereotypy in the deer mouse as an animal model of obsessive-compulsive disorder (OCD): Response to escitalopram treatment and basal serotonin transporter (SERT) density
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Neuroanatomy and Neuroimaging of Anxiety Disorders
Neuroimaging methods can be used to examine functional brain differences between healthy individuals and those with anxiety disorders. After the brain regions implicated in the pathophysiology of anxiety disorders (e.g., amygdalo-cortical circuitry) are reviewed, neuroimaging studies of posttraumatic stress disorder (PTSD), social anxiety disorder (SAD), specific phobia (SP), and panic disorder (PD) that report activations in these regions are discussed. Studies of obsessive-compulsive disorder (OCD) implicate a distinct neurocircuitry profile (i.e., cortico-striatal-thalamic circuit) compared to the other anxiety disorders. Few neuroimaging studies of generalized anxiety disorder (GAD) have been conducted. In addition, results from functional connectivity analyses and the effects of treatment on neuroimaging findings are summarized
Structural evidence for involvement of a left amygdala-orbitofrontal network in subclinical anxiety
An Examination of Rostral Anterior Cingulate Cortex Function and Neurochemistry in Obsessive–Compulsive Disorder
Analysis of Shared Heritability in Common Disorders of the Brain
Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology