323 research outputs found
supplementary_file – Supplemental material for Fractionation of impulsive and compulsive trans-diagnostic phenotypes and their longitudinal associations
No full textSupplemental material, supplementary_file for Fractionation of impulsive and compulsive trans-diagnostic phenotypes and their longitudinal associations by Samuel R Chamberlain, Jeggan Tiego, Leonardo F Fontenelle, Roxanne Hook, Linden Parkes, Rebecca Segrave, Tobias U Hauser, Ray J Dolan, Ian M Goodyer, Ed Bullmore, Jon E Grant and Murat Yücel in Australian & New Zealand Journal of Psychiatry</p
Integrating evidence from neuroimaging and neuropsychological studies of obsessive-compulsive disorder: the orbitofronto-striatal model revisited
No full textObsessive-compulsive disorder (OCD) is a common, heritable and disabling neuropsychiatric disorder. Theoretical models suggest that OCD is underpinned by functional and structural abnormalities in orbitofronto-striatal circuits. Evidence from cognitive and neuroimaging studies (functional and structural magnetic resonance imaging (MRI) and positron emission tomography (PET)) have generally been taken to be supportive of these theoretical models; however, results from these studies have not been entirely congruent with each other. With the advent of whole brain-based structural imaging techniques, such as voxel-based morphometry and multivoxel analyses, we consider it timely to assess neuroimaging findings to date, and to examine their compatibility with cognitive studies and orbitofronto-striatal models. As part of this assessment, we performed a quantitative, voxel-level meta-analysis of functional MRI findings, which revealed consistent abnormalities in orbitofronto-striatal and other additional areas in OCD. This review also considers the evidence for involvement of other brain areas outside orbitofronto-striatal regions in OCD, the limitations of current imaging techniques, and how future developments in imaging may aid our understanding of OCD.</p
Broadband criticality of human brain network synchronization
No full textSelf-organized criticality is an attractive model for human brain dynamics, but there has been little direct evidence for its existence in large-scale systems measured by neuroimaging. In general, critical systems are associated with fractal or power law scaling, long-range correlations in space and time, and rapid reconfiguration in response to external inputs. Here, we consider two measures of phase synchronization: the phase-lock interval, or duration of coupling between a pair of (neurophysiological) processes, and the lability of global synchronization of a (brain functional) network. Using computational simulations of two mechanistically distinct systems displaying complex dynamics, the Ising model and the Kuramoto model, we show that both synchronization metrics have power law probability distributions specifically when these systems are in a critical state. We then demonstrate power law scaling of both pairwise and global synchronization metrics in functional MRI and magnetoencephalographic data recorded from normal volunteers under resting conditions. These results strongly suggest that human brain functional systems exist in an endogenous state of dynamical criticality, characterized by a greater than random probability of both prolonged periods of phase-locking and occurrence of large rapid changes in the state of global synchronization, analogous to the neuronal “avalanches” previously described in cellular systems. Moreover, evidence for critical dynamics was identified consistently in neurophysiological systems operating at frequency intervals ranging from 0.05–0.11 to 62.5–125 Hz, confirming that criticality is a property of human brain functional network organization at all frequency intervals in the brain's physiological bandwidth
Brain network analysis : separating cost from topology using cost-integration
No full textA statistically principled way of conducting brain network analysis is still lacking. Comparison of different populations of
brain networks is hard because topology is inherently dependent on wiring cost, where cost is defined as the number of
edges in an unweighted graph. In this paper, we evaluate the benefits and limitations associated with using cost-integrated
topological metrics. Our focus is on comparing populations of weighted undirected graphs that differ in mean association
weight, using global efficiency. Our key result shows that integrating over cost is equivalent to controlling for any
monotonic transformation of the weight set of a weighted graph. That is, when integrating over cost, we eliminate the
differences in topology that may be due to a monotonic transformation of the weight set. Our result holds for any
unweighted topological measure, and for any choice of distribution over cost levels. Cost-integration is therefore helpful in
disentangling differences in cost from differences in topology. By contrast, we show that the use of the weighted version of
a topological metric is generally not a valid approach to this problem. Indeed, we prove that, under weak conditions, the
use of the weighted version of global efficiency is equivalent to simply comparing weighted costs. Thus, we recommend the
reporting of (i) differences in weighted costs and (ii) differences in cost-integrated topological measures with respect to
different distributions over the cost domain. We demonstrate the application of these techniques in a re-analysis of an fMRI
working memory task. We also provide a Monte Carlo method for approximating cost-integrated topological measures.
Finally, we discuss the limitations of integrating topology over cost, which may pose problems when some weights are zero,
when multiplicities exist in the ranks of the weights, and when one expects subtle cost-dependent topological differences,
which could be masked by cost-integration
Functional magnetic resonance imaging in psychiatry: where are we now and where are we going?
No full textEfficiency and cost of economical brain functional networks.
No full textBrain anatomical networks are sparse, complex, and have economical small-world properties. We investigated the efficiency and cost of human brain functional networks measured using functional magnetic resonance imaging (fMRI) in a factorial design: two groups of healthy old (N = 11; mean age = 66.5 years) and healthy young (N = 15; mean age = 24.7 years) volunteers were each scanned twice in a no-task or "resting" state following placebo or a single dose of a dopamine receptor antagonist (sulpiride 400 mg). Functional connectivity between 90 cortical and subcortical regions was estimated by wavelet correlation analysis, in the frequency interval 0.06-0.11 Hz, and thresholded to construct undirected graphs. These brain functional networks were small-world and economical in the sense of providing high global and local efficiency of parallel information processing for low connection cost. Efficiency was reduced disproportionately to cost in older people, and the detrimental effects of age on efficiency were localised to frontal and temporal cortical and subcortical regions. Dopamine antagonism also impaired global and local efficiency of the network, but this effect was differentially localised and did not interact with the effect of age. Brain functional networks have economical small-world properties-supporting efficient parallel information transfer at relatively low cost-which are differently impaired by normal aging and pharmacological blockade of dopamine transmission
Erratum: Complex brain networks: graph theoretical analysis of structural and functional systems
No full textNeurocognitive endophenotypes of obsessive-compulsive disorder
No full textEndophenotypes (intermediate phenotypes) are objective, heritable, quantitative traits hypothesized to represent genetic risk for polygenic disorders at more biologically tractable levels than distal behavioural and clinical phenotypes. It is theorized that endophenotype models of disease will help to clarify both diagnostic classification and aetiological understanding of complex brain disorders such as obsessive-compulsive disorder (OCD). To investigate endophenotypes in OCD, we measured brain structure using magnetic resonance imaging (MRI), and behavioural performance on a response inhibition task (Stop-Signal) in 31 OCD patients, 31 of their unaffected first-degree relatives, and 31 unrelated matched controls. Both patients and relatives had delayed response inhibition on the Stop-Signal task compared with healthy controls. We used a multivoxel analysis method (partial least squares) to identify large-scale brain systems in which anatomical variation was associated with variation in performance on the response inhibition task. Behavioural impairment on the Stop-Signal task, occurring predominantly in patients and relatives, was significantly associated with reduced grey matter in orbitofrontal and right inferior frontal regions and increased grey matter in cingulate, parietal and striatal regions. A novel permutation test indicated significant familial effects on variation of the MRI markers of inhibitory processing, supporting the candidacy of these brain structural systems as endophenotypes of OCD. In summary, structural variation in large-scale brain systems related to motor inhibitory control may mediate genetic risk for OCD, representing the first evidence for a neurocognitive endophenotype of OCD.</p
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