612 results found
Douaud G, Jbabdi S, Behrens TEJ, et al., 2011, DTI measures in crossing-fibre areas: Increased diffusion anisotropy reveals early white matter alteration in MCI and mild Alzheimer's disease, NEUROIMAGE, Vol: 55, Pages: 880-890, ISSN: 1053-8119
Dennis A, Bosnell R, Dawes H, et al., 2011, Cognitive Context Determines Dorsal Premotor Cortical Activity During Hand Movement in Patients After Stroke, STROKE, Vol: 42, Pages: 1056-1061, ISSN: 0039-2499
Valsasina P, Rocca MA, Absinta M, et al., 2011, A multicentre study of motor functional connectivity changes in patients with multiple sclerosis, EUROPEAN JOURNAL OF NEUROSCIENCE, Vol: 33, Pages: 1256-1263, ISSN: 0953-816X
Tomassini V, Jbabdi S, Kincses ZT, et al., 2011, Structural and Functional Bases for Individual Differences in Motor Learning, HUMAN BRAIN MAPPING, Vol: 32, Pages: 494-508, ISSN: 1065-9471
Owen DRJ, Piccini P, Matthews PM, 2011, Towards molecular imaging of multiple sclerosis, MULTIPLE SCLEROSIS JOURNAL, Vol: 17, Pages: 262-272, ISSN: 1352-4585
Bush WS, McCauley JL, DeJager PL, et al., 2011, A knowledge-driven interaction analysis reveals potential neurodegenerative mechanism of multiple sclerosis susceptibility, Genes and Immunity, Vol: 12, Pages: 335-340, ISSN: 1466-4879
Gene–gene interactions are proposed as an important component of the genetic architecture of complex diseases, and are just beginning to be evaluated in the context of genome-wide association studies (GWAS). In addition to detecting epistasis, a benefit to interaction analysis is that it also increases power to detect weak main effects. We conducted a knowledge-driven interaction analysis of a GWAS of 931 multiple sclerosis (MS) trios to discover gene–gene interactions within established biological contexts. We identify heterogeneous signals, including a gene–gene interaction between CHRM3 (muscarinic cholinergic receptor 3) and MYLK (myosin light-chain kinase) (joint P=0.0002), an interaction between two phospholipase C-β isoforms, PLCβ1 and PLCβ4 (joint P=0.0098), and a modest interaction between ACTN1 (actinin alpha 1) and MYH9 (myosin heavy chain 9) (joint P=0.0326), all localized to calcium-signaled cytoskeletal regulation. Furthermore, we discover a main effect (joint P=5.2E−5) previously unidentified by single-locus analysis within another related gene, SCIN (scinderin), a calcium-binding cytoskeleton regulatory protein. This work illustrates that knowledge-driven interaction analysis of GWAS data is a feasible approach to identify new genetic effects. The results of this study are among the first gene–gene interactions and non-immune susceptibility loci for MS. Further, the implicated genes cluster within inter-related biological mechanisms that suggest a neurodegenerative component to MS.
James A, Hough M, James S, et al., 2011, Structural brain and neuropsychometric changes associated with pediatric bipolar disorder with psychosis, BIPOLAR DISORDERS, Vol: 13, Pages: 16-27, ISSN: 1398-5647
Wang JH, Pappas D, De Jager PL, et al., 2011, Modeling the cumulative genetic risk for multiple sclerosis from genome-wide association data, GENOME MEDICINE, Vol: 3, ISSN: 1756-994X
Background:Multiple sclerosis (MS) is the most common cause of chronic neurologic disability beginning in early tomiddle adult life. Results from recent genome-wide association studies (GWAS) have substantially lengthened the list ofdisease loci and provide convincing evidence supporting a multifactorial and polygenic model of inheritance.Nevertheless, the knowledge of MS genetics remains incomplete, with many risk alleles still to be revealed.Methods:We used a discovery GWAS dataset (8,844 samples, 2,124 cases and 6,720 controls) and a multi-step logisticregression protocol to identify novel genetic associations. The emerging genetic profile included 350 independentmarkers and was used to calculate and estimate the cumulative genetic risk in an independent validation dataset (3,606samples). Analysis of covariance (ANCOVA) was implemented to compare clinical characteristics of individuals withvarious degrees of genetic risk. Gene ontology and pathway enrichment analysis was done using the DAVID functionalannotation tool, the GO Tree Machine, and the Pathway-Express profiling tool.Results:In the discovery dataset, the median cumulative genetic risk (P-Hat) was 0.903 and 0.007 in the case andcontrol groups, respectively, together with 79.9% classification sensitivity and 95.8% specificity. The identified profileshows a significant enrichment of genes involved in the immune response, cell adhesion, cell communication/signaling, nervous system development, and neuronal signaling, including ionotropic glutamate receptors, whichhave been implicated in the pathological mechanism driving neurodegeneration. In the validation dataset, themedian cumulative genetic risk was 0.59 and 0.32 in the case and control groups, respectively, with classificationsensitivity 62.3% and specificity 75.9%. No differences in disease progression or T2-lesion volumes were observedamong four levels of predicted genetic risk groups (high, medium, low, misclassified). On the other hand, asignifican
Owen DRJ, Matthews PM, 2011, IMAGING BRAIN MICROGLIAL ACTIVATION USING POSITRON EMISSION TOMOGRAPHY AND TRANSLOCATOR PROTEIN-SPECIFIC RADIOLIGANDS, BIOMARKERS OF NEUROLOGICAL AND PSYCHIATRIC DISEASE, Vol: 101, Pages: 19-39, ISSN: 0074-7742
Crofts JJ, Higham DJ, Bosnell R, et al., 2011, Network analysis detects changes in the contralesional hemisphere following stroke, NEUROIMAGE, Vol: 54, Pages: 161-169, ISSN: 1053-8119
Tomassini V, Johansen-Berg H, Leonardi L, et al., 2011, Preservation of motor skill learning in patients with multiple sclerosis, MULTIPLE SCLEROSIS JOURNAL, Vol: 17, Pages: 103-115, ISSN: 1352-4585
Filippini N, Ebmeier KP, MacIntosh BJ, et al., 2011, Differential effects of the APOE genotype on brain function across the lifespan, NEUROIMAGE, Vol: 54, Pages: 602-610, ISSN: 1053-8119
Wahjoepramono EJ, Wijaya LK, Taddei K, et al., 2011, Direct Exposure of Guinea Pig CNS to Human Luteinizing Hormone Increases Cerebrospinal Fluid and Cerebral Beta Amyloid Levels, NEUROENDOCRINOLOGY, Vol: 94, Pages: 313-322, ISSN: 0028-3835
Owen DR, Gunn RN, Rabiner EA, et al., 2011, Mixed-affinity binding in humans with 18-kDa translocator protein ligands, J Nucl Med, Vol: 52, Pages: 24-32, ISSN: 1535-5667
11C-PBR28 PET can detect the 18-kDa translocator protein (TSPO) expressed within macrophages. However, quantitative evaluation of the signal in brain tissue from donors with multiple sclerosis (MS) shows that PBR28 binds the TSPO with high affinity (binding affinity [Ki], approximately 4 nM), low affinity (Ki, approximately 200 nM), or mixed affinity (2 sites with Ki, approximately 4 nM and approximately 300 nM). Our study tested whether similar binding behavior could be detected in brain tissue from donors with no history of neurologic disease, with TSPO-binding PET ligands other than 11C-PBR28, for TSPO present in peripheral blood, and with human brain PET data acquired in vivo with 11C-PBR28. METHODS: The affinity of TSPO ligands was measured in the human brain postmortem from donors with a history of MS (n=13), donors without any history of neurologic disease (n=20), and in platelets from healthy volunteers (n=13). Binding potential estimates from thirty-five 11C-PBR28 PET scans from an independent sample of healthy volunteers were analyzed using a gaussian mixture model. RESULTS: Three binding affinity patterns were found in brains from subjects without neurologic disease in similar proportions to those reported previously from studies of MS brains. TSPO ligands showed substantial differences in affinity between subjects classified as high-affinity binders (HABs) and low-affinity binders (LABs). Differences in affinity between HABs and LABs are approximately 50-fold with PBR28, approximately 17-fold with PBR06, and approximately 4-fold with DAA1106, DPA713, and PBR111. Where differences in affinity between HABs and LABs were low ( approximately 4-fold), distinct affinities were not resolvable in binding curves for mixed-affinity binders (MABs), which appeared to express 1 class of sites with an affinity approximately equal to the mean of those for HABs and LABs. Mixed-affinity binding was detected in platelets from an independent sample (HAB, 69%; MAB, 31%), al
Rabiner EA, Beaver J, Makwana A, et al., 2011, Pharmacological differentiation of opioid receptor antagonists by molecular and functional imaging of target occupancy and food reward-related brain activation in humans, Mol Psychiatry, Vol: 16, Pages: 826-785, ISSN: 1476-5578
Opioid neurotransmission has a key role in mediating reward-related behaviours. Opioid receptor (OR) antagonists, such as naltrexone (NTX), can attenuate the behaviour-reinforcing effects of primary (food) and secondary rewards. GSK1521498 is a novel OR ligand, which behaves as an inverse agonist at the mu-OR sub-type. In a sample of healthy volunteers, we used [(11)C]-carfentanil positron emission tomography to measure the OR occupancy and functional magnetic resonance imaging (fMRI) to measure activation of brain reward centres by palatable food stimuli before and after single oral doses of GSK1521498 (range, 0.4-100 mg) or NTX (range, 2-50 mg). GSK1521498 had high affinity for human brain ORs (GSK1521498 effective concentration 50 = 7.10 ng ml(-1)) and there was a direct relationship between receptor occupancy (RO) and plasma concentrations of GSK1521498. However, for both NTX and its principal active metabolite in humans, 6-beta-NTX, this relationship was indirect. GSK1521498, but not NTX, significantly attenuated the fMRI activation of the amygdala by a palatable food stimulus. We thus have shown how the pharmacological properties of OR antagonists can be characterised directly in humans by a novel integration of molecular and functional neuroimaging techniques. GSK1521498 was differentiated from NTX in terms of its pharmacokinetics, target affinity, plasma concentration-RO relationships and pharmacodynamic effects on food reward processing in the brain. Pharmacological differentiation of these molecules suggests that they may have different therapeutic profiles for treatment of overeating and other disorders of compulsive consumption.
Antoniades A, Matthews PM, Pattichis CS, et al., 2010, A computationally fast measure of epistasis for 2 SNPs and a categorical phenotype, Pages: 6194-6197
Complex diseases may be caused by interactions or combined effects between multiple genetic and environmental factors. One of the main limitations of testing for interaction between genetic loci in large whole genome studies is the high computational cost of performing such analyses. In this study a new methodology for interaction testing (commonly referred to in genetics as the epistatic effect) between two single nucleotide polymorphisms (SNPs) and a categorical phenotype is presented. It is shown that it provides reasonable approximations with a significantly shorter run time. The proposed measure based on the Pearson's chi-square additive property is compared to fitting a logistic regression model on a randomly selected subset of 218 SNP loci from a study that included 550,000 SNPs). For each possible pair of SNPs a chi-square test for the epistatic effect on case-control status is estimated by fitting a logistic regression model, and compared to the results of the proposed method. Results indicate strong agreement (Pearson's correlation r>0.95) while the proposed method is found to be 20 times faster. This provides a significant pragmatic advantage for the proposed method since the number of tests for epistasis can now be increased by a factor of 20 while the computational cost remains the same. © 2010 IEEE.
Inkster B, Nichols TE, Saemann PG, et al., 2010, Pathway-based approaches to imaging genetics association studies: Wnt signaling, GSK3beta substrates and major depression, NEUROIMAGE, Vol: 53, Pages: 908-917, ISSN: 1053-8119
Sloan HL, Austin VC, Blamire AM, et al., 2010, Regional differences in neurovascular coupling in rat brain as determined by fMRI and electrophysiology, NEUROIMAGE, Vol: 53, Pages: 399-411, ISSN: 1053-8119
Wise RG, Pattinson KTS, Bulte DP, et al., 2010, Measurement of relative cerebral blood volume using BOLD contrast and mild hypoxic hypoxia, MAGNETIC RESONANCE IMAGING, Vol: 28, Pages: 1129-1134, ISSN: 0730-725X
Menke RA, Jbabdi S, Miller KL, et al., 2010, Connectivity-based segmentation of the substantia nigra in human and its implications in Parkinson's disease, NEUROIMAGE, Vol: 52, Pages: 1175-1180, ISSN: 1053-8119
Menke RA, Jbabdi S, Miller KL, et al., 2010, Connectivity-based segmentation of the substantia nigra in human and its implications in Parkinson's disease., Neuroimage, Vol: 52, Pages: 1175-1180
The aims of this study were to i) identify substantia nigra subregions i.e. pars reticulata (SNr) and pars compacta (SNc), in human, and ii) to assess volumetric changes in these subregions in the diagnosis of Parkinson's disease. Current MR imaging techniques are unable to distinguish SNr and SNc. Segmentation of these regions may be clinically useful in Parkinson's disease (PD) as substantia nigra is invariably affected in PD. We acquired quantitative T1 as well as diffusion tensor imaging (DTI) data from ten healthy subjects and ten PD patients. For each subject, the left and right SN were manually outlined on T1 images and then classified into two discrete regions based on the characteristics of their connectivity with the rest of the brain using an automated clustering method on the DTI data. We identified two regions in each subjects' SN: an internal region that is likely to correspond with SNc because it was mainly connected with posterior striatum, pallidum, anterior thalamus, and prefrontal cortex; and an external region that corresponds with SNr because it was chiefly connected with posterior thalamus, ventral thalamus, and motor cortex. Volumetric study of these regions in PD patients showed a general atrophy in PD particularly in the right SNr. This pilot study showed that automated DTI-based parcellation of SN subregions may provide a useful tool for in-vivo identification of SNc and SNr and might therefore assist to detect changes that occur in patients with PD.
Baranzini SE, Srinivasan R, Khankhanian P, et al., 2010, Genetic variation influences glutamate concentrations in brains of patients with multiple sclerosis, BRAIN, Vol: 133, Pages: 2603-2611, ISSN: 0006-8950
Cole DM, Beckmann CF, Long CJ, et al., 2010, Nicotine replacement in abstinent smokers improves cognitive withdrawal symptoms with modulation of resting brain network dynamics, NEUROIMAGE, Vol: 52, Pages: 590-599, ISSN: 1053-8119
Owen DR, Rabiner EA, Gunn RN, et al., 2010, PBR28, PBR06 and PBR111 bind two distinct TSPO sites in human brain tissue, 8th International Symposium on Functional Neuroreceptor Mapping of the Living Brain, Publisher: ACADEMIC PRESS INC ELSEVIER SCIENCE, Pages: S30-S31, ISSN: 1053-8119
Kasperaviciute D, Catarino CB, Heinzen EL, et al., 2010, Common genetic variation and susceptibility to partial epilepsies: a genome-wide association study, Brain, Vol: 133, Pages: 2136-2147, ISSN: 1460-2156
Partial epilepsies have a substantial heritability. However, the actual genetic causes are largely unknown. In contrast to many other common diseases for which genetic association-studies have successfully revealed common variants associated with disease risk, the role of common variation in partial epilepsies has not yet been explored in a well-powered study. We undertook a genome-wide association-study to identify common variants which influence risk for epilepsy shared amongst partial epilepsy syndromes, in 3445 patients and 6935 controls of European ancestry. We did not identify any genome-wide significant association. A few single nucleotide polymorphisms may warrant further investigation. We exclude common genetic variants with effect sizes above a modest 1.3 odds ratio for a single variant as contributors to genetic susceptibility shared across the partial epilepsies. We show that, at best, common genetic variation can only have a modest role in predisposition to the partial epilepsies when considered across syndromes in Europeans. The genetic architecture of the partial epilepsies is likely to be very complex, reflecting genotypic and phenotypic heterogeneity. Larger meta-analyses are required to identify variants of smaller effect sizes (odds ratio <1.3) or syndrome-specific variants. Further, our results suggest research efforts should also be directed towards identifying the multiple rare variants likely to account for at least part of the heritability of the partial epilepsies. Data emerging from genome-wide association-studies will be valuable during the next serious challenge of interpreting all the genetic variation emerging from whole-genome sequencing studies.
Zarei M, Patenaude B, Damoiseaux J, et al., 2010, Combining shape and connectivity analysis: An MRI study of thalamic degeneration in Alzheimer's disease (vol 49, pg 1, 2010), NEUROIMAGE, Vol: 51, Pages: 940-940, ISSN: 1053-8119
Heinzen EL, Radtke RA, Urban TJ, et al., 2010, Rare Deletions at 16p13.11 Predispose to a Diverse Spectrum of Sporadic Epilepsy Syndromes, AMERICAN JOURNAL OF HUMAN GENETICS, Vol: 86, Pages: 707-718, ISSN: 0002-9297
Pomeroy IM, Jordan EK, Frank JA, et al., 2010, Focal and diffuse cortical degenerative changes in a marmoset model of multiple sclerosis, MULTIPLE SCLEROSIS JOURNAL, Vol: 16, Pages: 537-548, ISSN: 1352-4585
Matthews PM, 2010, IMAGING FOR TRANSLATIONAL PHARMACOLOGY, IRISH JOURNAL OF MEDICAL SCIENCE, Vol: 179, Pages: S99-S100, ISSN: 0021-1265
Tzimopoulou S, Cunningham VJ, Nichols TE, et al., 2010, A Multi-Center Randomized Proof-of-Concept Clinical Trial Applying [F-18]FDG-PET for Evaluation of Metabolic Therapy with Rosiglitazone XR in Mild to Moderate Alzheimer's Disease, JOURNAL OF ALZHEIMERS DISEASE, Vol: 22, Pages: 1241-1256, ISSN: 1387-2877
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.