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    ABSTRACT: The non-competitive N-methyl-D-aspartate receptor antagonist ketamine leads to transient psychosis-like symptoms and impairments in oculomotor performance in healthy volunteers. This study examined whether the adverse effects of ketamine on oculomotor performance can be reversed by the atypical antipsychotic risperidone. In this randomized double-blind, placebo-controlled study, 72 healthy participants performed smooth pursuit eye movements (SPEM), prosaccades (PS) and antisaccades (AS) while being randomly assigned to one of four drug groups (intravenous 100 ng ml(-1) ketamine, 2 mg oral risperidone, 100 ng ml(-1) ketamine plus 2 mg oral risperidone, placebo). Drug administration did not lead to harmful adverse events. Ketamine increased saccadic frequency and decreased velocity gain of SPEM (all P<0.01) but had no significant effects on PS or AS (all P0.07). An effect of risperidone was observed for amplitude gain and peak velocity of PS and AS, indicating hypometric gain and slower velocities compared with placebo (both P0.04). No ketamine by risperidone interactions were found (all P0.26). The results confirm that the administration of ketamine produces oculomotor performance deficits similar in part to those seen in schizophrenia. The atypical antipsychotic risperidone did not reverse ketamine-induced deteriorations. These findings do not support the cognitive enhancing potential of risperidone on oculomotor biomarkers in this model system of schizophrenia and point towards the importance of developing alternative performance-enhancing compounds to optimise pharmacological treatment of schizophrenia.
    Translational Psychiatry 12/2013; 3(12):e334. DOI:10.1038/tp.2013.109
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    ABSTRACT: The anterior insula (AI) plays a key role in affective processing, and insular dysfunction has been noted in several clinical conditions. Real-time functional MRI neurofeedback (rtfMRI-NF) provides a means of helping people learn to self-regulate activation in this brain region. Using the Blood Oxygenated Level Dependant (BOLD) signal from the right AI (RAI) as neurofeedback, we trained participants to increase RAI activation. In contrast, another group of participants were shown 'control' feedback from another brain area. Pre- and post- training affective probes were shown, with subjective ratings and skin conductance response (SCR) measured. We also investigated a reward-related reinforcement learning model of rtfMRI-NF In contrast to controls, we hypothesised a positive linear increase in RAI activation in participants shown feedback from this region, alongside increases in valence ratings and skin conductance response (SCR) to affective probes. Hypothesis-driven analyses showed a significant interaction between the RAI / control neurofeedback groups and the effect of self-regulation. Whole-brain analyses revealed a significant linear increase in RAI activation across four training runs in the group who received feedback from RAI. Increased activation was also observed in the caudate body and thalamus, likely representing feedback-related learning. No positive linear trend was observed in the RAI in the group receiving control feedback, suggesting that these data are not a general effect of cognitive strategy or control feedback. The control group did, however, show diffuse activation across the putamen, caudate and posterior insula which may indicate the representation of false feedback. No significant training-related behavioural differences were observed for valence ratings, or SCR. In addition, correlational analyses based on a reinforcement learning model showed the dorsal anterior cingulate cortex underpinned learning in both groups. In summary, these data demonstrate that it is possible to regulate the RAI using rtfMRI-NF within one scanning session, and that such reward-related learning is mediated by the dorsal anterior cingulate.
    NeuroImage 11/2013; 88. DOI:10.1016/j.neuroimage.2013.10.069
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    ABSTRACT: Diffusion tensor imaging (DTI) methods are widely used to reconstruct white matter trajectories and to quantify tissue changes using the average diffusion properties of each brain voxel. Spherical deconvolution (SD) methods have been developed to overcome the limitations of the diffusion tensor model in resolving crossing fibers and to improve tractography reconstructions. However, the use of SD methods to obtain quantitative indices of white matter integrity has not been extensively explored. In this study, we show that the hindrance modulated orientational anisotropy (HMOA) index, defined as the absolute amplitude of each lobe of the fiber orientation distribution, can be used as a compact measure to characterize the diffusion properties along each fiber orientation in white matter regions with complex organization. We demonstrate that the HMOA is highly sensitive to changes in fiber diffusivity (e.g., myelination processes or axonal loss) and to differences in the microstructural organization of white matter like axonal diameter and fiber dispersion. Using simulations to describe diffusivity changes observed in normal brain development and disorders, we observed that the HMOA is able to identify white matter changes that are not detectable with conventional DTI indices. We also show that the HMOA index can be used as an effective threshold for in vivo data to improve tractography reconstructions and to better map white matter complexity inside the brain. In conclusion, the HMOA represents a true tract-specific and sensitive index and provides a compact characterization of white matter diffusion properties with potential for widespread application in normal and clinical populations. Hum Brain Mapp, 2012. © 2012 Wiley Periodicals, Inc.
    Human Brain Mapping 10/2013; 34(10). DOI:10.1002/hbm.22080
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    ABSTRACT: After 140 years from the discovery of Golgi's black reaction, the study of connectivity of the cerebellum remains a fascinating yet challenging task. Current histological techniques provide powerful methods for unravelling local axonal architecture, but the relatively low volume of data that can be acquired in a reasonable amount of time limits their application to small samples. State-of-the-art in vivo magnetic resonance imaging (MRI) methods, such as diffusion tractography techniques, can reveal trajectories of the major white matter pathways, but their correspondence with underlying anatomy is yet to be established. Hence, a significant gap exists between these two approaches as neither of them can adequately describe the three-dimensional complexity of fibre architecture at the level of the mesoscale (from a few millimetres to micrometres). In this study, we report the application of MR diffusion histology and micro-tractography methods to reveal the combined cytoarchitectural organisation and connectivity of the human cerebellum at a resolution of 100-μm (2 nl/voxel volume). Results show that the diffusion characteristics for each layer of the cerebellar cortex correctly reflect the known cellular composition and its architectural pattern. Micro-tractography also reveals details of the axonal connectivity of individual cerebellar folia and the intra-cortical organisation of the different cerebellar layers. The direct correspondence between MR diffusion histology and micro-tractography with immunohistochemistry indicates that these approaches have the potential to complement traditional histology techniques by providing a non-destructive, quantitative and three-dimensional description of the microstructural organisation of the healthy and pathological tissue.
    The Cerebellum 08/2013; 12(6). DOI:10.1007/s12311-013-0503-x
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    ABSTRACT: Graves' orbitopathy (GO) is a complication in Graves' disease (GD) but mechanistic insights into pathogenesis remain unresolved, hampered by lack of animal model. The TSH receptor (TSHR) and perhaps IGF-1 receptor (IGF-1R) are considered relevant antigens. We show that genetic immunization of human TSHR (hTSHR) A-subunit plasmid leads to extensive remodeling of orbital tissue, recapitulating GO. Female BALB/c mice immunized with hTSHR A-subunit or control plasmids by in vivo muscle electroporation were evaluated for orbital remodeling by histopathology and magnetic resonance imaging (MRI). Antibodies to TSHR and IGF-1R were present in animals challenged with hTSHR A-subunit plasmid, with predominantly TSH blocking antibodies and were profoundly hypothyroid. Orbital pathology was characterized by interstitial inflammation of extraocular muscles with CD3+ T cells, F4/80+ macrophages, and mast cells, accompanied by glycosaminoglycan deposition with resultant separation of individual muscle fibers. Some animals showed heterogeneity in orbital pathology with 1) large infiltrate surrounding the optic nerve or 2) extensive adipogenesis with expansion of retrobulbar adipose tissue. A striking finding that underpins the new model were the in vivo MRI scans of mouse orbital region that provided clear and quantifiable evidence of orbital muscle hypertrophy with protrusion (proptosis) of the eye. Additionally, eyelid manifestations of chemosis, including dilated and congested orbital blood vessels, were visually apparent. Immunization with control plasmids failed to show any orbital pathology. Overall, these findings support TSHR as the pathogenic antigen in GO. Development of a new preclinical model will facilitate molecular investigations on GO and evaluation of new therapeutic interventions.
    Endocrinology 07/2013; 154(9). DOI:10.1210/en.2013-1576
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    ABSTRACT: Progressive supranuclear palsy (PSP), multiple system atrophy (MSA) and idiopathic Parkinson's disease (IPD) can be clinically indistinguishable, especially in the early stages, despite distinct patterns of molecular pathology. Structural neuroimaging holds promise for providing objective biomarkers for discriminating these diseases at the single subject level but all studies to date have reported incomplete separation of disease groups. In this study, we employed multi-class pattern recognition to assess the value of anatomical patterns derived from a widely available structural neuroimaging sequence for automated classification of these disorders. To achieve this, 17 patients with PSP, 14 with IPD and 19 with MSA were scanned using structural MRI along with 19 healthy controls (HCs). An advanced probabilistic pattern recognition approach was employed to evaluate the diagnostic value of several pre-defined anatomical patterns for discriminating the disorders, including: (i) a subcortical motor network; (ii) each of its component regions and (iii) the whole brain. All disease groups could be discriminated simultaneously with high accuracy using the subcortical motor network. The region providing the most accurate predictions overall was the midbrain/brainstem, which discriminated all disease groups from one another and from HCs. The subcortical network also produced more accurate predictions than the whole brain and all of its constituent regions. PSP was accurately predicted from the midbrain/brainstem, cerebellum and all basal ganglia compartments; MSA from the midbrain/brainstem and cerebellum and IPD from the midbrain/brainstem only. This study demonstrates that automated analysis of structural MRI can accurately predict diagnosis in individual patients with Parkinsonian disorders, and identifies distinct patterns of regional atrophy particularly useful for this process.
    PLoS ONE 07/2013; 8(7):e69237. DOI:10.1371/journal.pone.0069237
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    ABSTRACT: To validate and add value to non-invasive imaging techniques, corresponding histology is required to establish biological correlates. We present an efficient semi-automated image-processing pipeline that uses immunohistochemically-stained sections to reconstruct a 3D brain volume from 2D histological images before registering these with the corresponding 3D in vivo magnetic resonance images (MRI). A multistep registration procedure that first aligns the "global" volume by using the center of mass and then applies a rigid and affine alignment based on signal intensities is described. This is applied to a training set of three rat brain volumes before being validated on three more normal brains. Application of the approach to register "abnormal" images from a rat model of stroke allows the neurobiological correlates of the variations in the hyper-intense MRI signal intensity caused by infarction to be investigated. For evaluation, corresponding anatomical landmarks in MR and histology were defined to measure the accuracy of registration. A registration error of 0.249mm (approximately one in-plane voxel dimension) was evident in healthy rat brains and 0.323mm in a rodent model of stroke. The proposed reconstruction and registration pipeline allows precise analysis of non-invasive MRI and corresponding microstructural histological features in 3D. We were thus able to interrogate histology to deduce the cause of MRI signal variations in the lesion cavity and the peri-infarct area.
    Journal of neuroscience methods 06/2013; DOI:10.1016/j.jneumeth.2013.06.003
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    ABSTRACT: Background Bipolar disorder (BD) is one of the leading causes of disability worldwide. Patients are further disadvantaged by delays in accurate diagnosis ranging between 5 and 10 years. We applied Gaussian process classifiers (GPCs) to structural magnetic resonance imaging (sMRI) data to evaluate the feasibility of using pattern recognition techniques for the diagnostic classification of patients with BD. Method GPCs were applied to gray (GM) and white matter (WM) sMRI data derived from two independent samples of patients with BD (cohort 1: n = 26; cohort 2: n = 14). Within each cohort patients were matched on age, sex and IQ to an equal number of healthy controls. Results The diagnostic accuracy of the GPC for GM was 73% in cohort 1 and 72% in cohort 2; the sensitivity and specificity of the GM classification were respectively 69% and 77% in cohort 1 and 64% and 99% in cohort 2. The diagnostic accuracy of the GPC for WM was 69% in cohort 1 and 78% in cohort 2; the sensitivity and specificity of the WM classification were both 69% in cohort 1 and 71% and 86% respectively in cohort 2. In both samples, GM and WM clusters discriminating between patients and controls were localized within cortical and subcortical structures implicated in BD. Conclusions Our results demonstrate the predictive value of neuroanatomical data in discriminating patients with BD from healthy individuals. The overlap between discriminative networks and regions implicated in the pathophysiology of BD supports the biological plausibility of the classifiers.
    Psychological Medicine 06/2013; 44(3):1-14. DOI:10.1017/S0033291713001013
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    ABSTRACT: Neuroimaging data are increasingly being used to predict potential outcomes or groupings, such as clinical severity, drug dose response, and transitional illness states. In these examples, the variable (target) we want to predict is ordinal in nature. Conventional classification schemes assume that the targets are nominal and hence ignore their ranked nature, whereas parametric and/or non-parametric regression models enforce a metric notion of distance between classes. Here, we propose a novel, alternative multivariate approach that overcomes these limitations - whole brain probabilistic ordinal regression using a Gaussian process framework. We applied this technique to two data sets of pharmacological neuroimaging data from healthy volunteers. The first study was designed to investigate the effect of ketamine on brain activity and its subsequent modulation with two compounds - lamotrigine and risperidone. The second study investigates the effect of scopolamine on cerebral blood flow and its modulation using donepezil. We compared ordinal regression to multi-class classification schemes and metric regression. Considering the modulation of ketamine with lamotrigine, we found ordinal regression significantly outperformed multi-class classification and metric regression in terms of accuracy and mean absolute error. However, for risperidone ordinal regression significantly outperformed metric regression but performed similarly to multi-class classification both in terms of accuracy and mean absolute error. For the scopolamine dataset, ordinal regression was found to outperform both multi-class and metric regression techniques considering the regional cerebral blood flow in the anterior cingulate cortex. Ordinal regression was thus the only method that performed well in all cases. Our results indicate the potential of an ordinal regression approach for neuroimaging data while providing a fully probabilistic framework with elegant approaches for model selection.
    NeuroImage 05/2013; 81(100). DOI:10.1016/j.neuroimage.2013.05.036
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    ABSTRACT: Previous evidence shows a reliable association between psychosis-prone (especially schizotypal) personality traits and performance on dopamine (DA)-sensitive tasks (e.g., prepulse inhibition and antisaccade). Here, we used blood oxygen level-dependent (BOLD) fMRI and an established procedural learning (PL) task to examine the dopaminergic basis of two aspects of psychosis-proneness (specific schizotypy and general psychoticism). Thirty healthy participants (final N = 26) underwent fMRI during a blocked, periodic sequence-learning task which, in previous studies, has been shown to reveal impaired performance in schizophrenia patients given drugs blocking the DA D2 receptor subtype (DRD2), and to correspond with manipulation of DA activity and elicit fronto-striatal-cerebellar activity in healthy people. Psychosis-proneness was indexed by the Psychoticism (P) scale of the Eysenck Personality Questionnaire-Revised (EPQ-R; 1991) and the Schizotypal Personality Scale (STA; 1984). EPQ-R Extraversion and Neuroticism scores were also examined to establish discriminant validity. We found a positive correlation between the two psychosis-proneness measures (r = 0.43), and a robust and unique positive association between EPQ-R P and BOLD signal in the putamen, caudate, thalamus, insula, and frontal regions. STA schizotypy score correlated positively with activity in the right middle temporal gyrus. As DA is a key transmitter in the basal ganglia, and the thalamus contains the highest levels of DRD2 receptors of all extrastriatal regions, our results support a dopaminergic basis of psychosis-proneness as measured by the EPQ-R Psychoticism.
    Frontiers in Human Neuroscience 04/2013; 7:130. DOI:10.3389/fnhum.2013.00130
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