45 results found
Cole JH, Annus T, Wilson LR, et al., 2017, Brain-predicted age in Down syndrome is associated with beta amyloid deposition and cognitive decline., Neurobiol Aging, Vol: 56, Pages: 41-49
Individuals with Down syndrome (DS) are more likely to experience earlier onset of multiple facets of physiological aging. This includes brain atrophy, beta amyloid deposition, cognitive decline, and Alzheimer's disease-factors indicative of brain aging. Here, we employed a machine learning approach, using structural neuroimaging data to predict age (i.e., brain-predicted age) in people with DS (N = 46) and typically developing controls (N = 30). Chronological age was then subtracted from brain-predicted age to generate a brain-predicted age difference (brain-PAD) score. DS participants also underwent [(11)C]-PiB positron emission tomography (PET) scans to index the levels of cerebral beta amyloid deposition, and cognitive assessment. Mean brain-PAD in DS participants' was +2.49 years, significantly greater than controls (p < 0.001). The variability in brain-PAD was associated with the presence and the magnitude of PiB-binding and levels of cognitive performance. Our study indicates that DS is associated with premature structural brain aging, and that age-related alterations in brain structure are associated with individual differences in the rate of beta amyloid deposition and cognitive impairment.
Age-associated disease and disability are placing a growing burden on society. However, ageing does not affect people uniformly. Hence, markers of the underlying biological ageing process are needed to help identify people at increased risk of age-associated physical and cognitive impairments and ultimately, death. Here, we present such a biomarker, 'brain-predicted age', derived using structural neuroimaging. Brain-predicted age was calculated using machine-learning analysis, trained on neuroimaging data from a large healthy reference sample (N=2001), then tested in the Lothian Birth Cohort 1936 (N=669), to determine relationships with age-associated functional measures and mortality. Having a brain-predicted age indicative of an older-appearing brain was associated with: weaker grip strength, poorer lung function, slower walking speed, lower fluid intelligence, higher allostatic load and increased mortality risk. Furthermore, while combining brain-predicted age with grey matter and cerebrospinal fluid volumes (themselves strong predictors) not did improve mortality risk prediction, the combination of brain-predicted age and DNA-methylation-predicted age did. This indicates that neuroimaging and epigenetics measures of ageing can provide complementary data regarding health outcomes. Our study introduces a clinically-relevant neuroimaging ageing biomarker and demonstrates that combining distinct measurements of biological ageing further helps to determine risk of age-related deterioration and death.Molecular Psychiatry advance online publication, 25 April 2017; doi:10.1038/mp.2017.62.
Cole JH, Underwood J, Caan MWA, et al., 2017, Increased brain-predicted aging in treated HIV disease, NEUROLOGY, Vol: 88, Pages: 1349-1357, ISSN: 0028-3878
Pardoe HR, Cole JH, Blackmon K, et al., 2017, Structural brain changes in medically refractory focal epilepsy resemble premature brain aging, EPILEPSY RESEARCH, Vol: 133, Pages: 28-32, ISSN: 0920-1211
Picchioni MM, Rijsdijk F, Toulopoulou T, et al., 2017, Familial and environmental influences on brain volumes in twins with schizophrenia, JOURNAL OF PSYCHIATRY & NEUROSCIENCE, Vol: 42, Pages: 122-130, ISSN: 1180-4882
De Simoni S, Kochaj R, Jenkins P, et al., 2016, Changes in cerebral blood flow and their relationship to cognition following traumatic brain injury, Publisher: TAYLOR & FRANCIS INC, Pages: 605-605, ISSN: 0269-9052
Jenkins P, De Simoni S, Fleminger J, et al., 2016, Disruption to the dopaminergic system following traumatic brain injury, Publisher: TAYLOR & FRANCIS INC, Pages: 670-670, ISSN: 0269-9052
Jenkins PO, De Simoni S, Fleminger J, et al., 2016, DISRUPTION TO THE DOPAMINERGIC SYSTEM AFTER TRAUMATIC BRAIN INJURY, Annual Meeting of the Association-of-British-Neurologists (ABN), Publisher: BMJ PUBLISHING GROUP, ISSN: 0022-3050
Jolly AE, De Simoni S, Cole JH, et al., 2016, Identifying cognitive impairment in TBI: A novel multivariate approach, Publisher: TAYLOR & FRANCIS INC, Pages: 518-518, ISSN: 0269-9052
Scott G, Ramlackhansingh AF, Edison P, et al., 2016, Amyloid pathology and axonal injury after brain trauma, NEUROLOGY, Vol: 86, Pages: 821-828, ISSN: 0028-3878
Su T, Caan MWA, Wit FWNM, et al., 2016, White matter structure alterations in HIV-1-infected men with sustained suppression of viraemia on treatment, AIDS, Vol: 30, Pages: 311-322, ISSN: 0269-9370
Su T, Wit FWNM, Caan MWA, et al., 2016, White matter hyperintensities in relation to cognition in HIV-infected men with sustained suppressed viral load on combination antiretroviral therapy, AIDS, Vol: 30, Pages: 2329-2339, ISSN: 0269-9370
Underwood J, Cole J, Sharp D, et al., 2016, Brain MRI changes associated with poorer cognitive function despite suppressive antiretroviral therapy, Publisher: WILEY-BLACKWELL, Pages: 6-6, ISSN: 1464-2662
Vera JH, Guo Q, Cole JH, et al., 2016, Neuroinflammation in treated HIV-positive individuals A TSPO PET study, NEUROLOGY, Vol: 86, Pages: 1425-1432, ISSN: 0028-3878
Wise T, Radua J, Via E, et al., 2016, Common and distinct patterns of grey-matter volume alteration in major depression and bipolar disorder: evidence from voxel-based meta-analysis., Mol Psychiatry
Finding robust brain substrates of mood disorders is an important target for research. The degree to which major depression (MDD) and bipolar disorder (BD) are associated with common and/or distinct patterns of volumetric changes is nevertheless unclear. Furthermore, the extant literature is heterogeneous with respect to the nature of these changes. We report a meta-analysis of voxel-based morphometry (VBM) studies in MDD and BD. We identified studies published up to January 2015 that compared grey matter in MDD (50 data sets including 4101 individuals) and BD (36 data sets including 2407 individuals) using whole-brain VBM. We used statistical maps from the studies included where available and reported peak coordinates otherwise. Group comparisons and conjunction analyses identified regions in which the disorders showed common and distinct patterns of volumetric alteration. Both disorders were associated with lower grey-matter volume relative to healthy individuals in a number of areas. Conjunction analysis showed smaller volumes in both disorders in clusters in the dorsomedial and ventromedial prefrontal cortex, including the anterior cingulate cortex and bilateral insula. Group comparisons indicated that findings of smaller grey-matter volumes relative to controls in the right dorsolateral prefrontal cortex and left hippocampus, along with cerebellar, temporal and parietal regions were more substantial in major depression. These results suggest that MDD and BD are characterised by both common and distinct patterns of grey-matter volume changes. This combination of differences and similarities has the potential to inform the development of diagnostic biomarkers for these conditions.Molecular Psychiatry advance online publication, 24 May 2016; doi:10.1038/mp.2016.72.
Chalavi S, Vissia EM, Giesen ME, et al., 2015, Abnormal Hippocampal Morphology in Dissociative Identity Disorder and Post-Traumatic Stress Disorder Correlates with Childhood Trauma and Dissociative Symptoms, HUMAN BRAIN MAPPING, Vol: 36, Pages: 1692-1704, ISSN: 1065-9471
Cole JH, 2015, The influence of HIV on brain age: Preliminary results from the Co-morBidity in Relation to AIDS (COBRA) collaboration, 12th International Symposium on the Neurobiology and Neuroendocrinology of Aging, Publisher: PERGAMON-ELSEVIER SCIENCE LTD, Pages: 98-98, ISSN: 0531-5565
Cole JH, Leech R, Sharp DJ, 2015, Prediction of Brain Age Suggests Accelerated Atrophy after Traumatic Brain Injury, ANNALS OF NEUROLOGY, Vol: 77, Pages: 571-581, ISSN: 0364-5134
Fu CHY, Legge RM, Cohen-Woods S, et al., 2015, Ethnic differences in BDNF Val66Met polymorphism Reply, Publisher: ROYAL COLLEGE OF PSYCHIATRISTS
Gregory S, Cole JH, Farmer RE, et al., 2015, Longitudinal Diffusion Tensor Imaging Shows Progressive Changes in White Matter in Huntington's Disease., J Huntingtons Dis, Vol: 4, Pages: 333-346
BACKGROUND: Huntington's disease is marked by progressive neuroanatomical changes, assumed to underlie the development of the disease's characteristic symptoms. Previous work has demonstrated longitudinal macrostructural white-matter atrophy, with some evidence of microstructural change focused in the corpus callosum. OBJECTIVE: To more accurately characterise longitudinal patterns, we examined white matter microstructural change using Diffusion Tensor Imaging (DTI) data from three timepoints over a 15 month period. METHODS: In 48 early-stage HD patients and 36 controls from the multi-site PADDINGTON project, diffusion tensor imaging (DTI) was employed to measure changes in fractional anisotropy (FA) and axial (AD) and radial diffusivity (RD) in 24 white matter regions-of-interest (ROIs). RESULTS: Cross-sectional analysis indicated widespread baseline between-group differences, with significantly decreased FA and increased AD and RD found in HD patients across multiple ROIs. Longitudinal rates of change differed significantly between HD patients and controls in the genu and body of corpus callosum, corona radiata and anterior limb of internal capsule. Change in RD in the body of the corpus callosum was significantly associated with baseline disease burden, but other clinical associations were not significant. CONCLUSIONS: We detected subtle longitudinal white matter changes in early HD patients. Progressive white matter abnormalities in HD may not be uniform throughout the brain, with some areas remaining static in the early symptomatic phase. Longer assessment periods across disease stages will help map this progressive trajectory.
Hobbs NZ, Farmer RE, Rees EM, et al., 2015, Short-interval observational data to inform clinical trial design in Huntington's disease., J Neurol Neurosurg Psychiatry, Vol: 86, Pages: 1291-1298
OBJECTIVES: To evaluate candidate outcomes for disease-modifying trials in Huntington's disease (HD) over 6-month, 9-month and 15-month intervals, across multiple domains. To present guidelines on rapid efficacy readouts for disease-modifying trials. METHODS: 40 controls and 61 patients with HD, recruited from four EU sites, underwent 3 T MRI and standard clinical and cognitive assessments at baseline, 6 and 15 months. Neuroimaging analysis included global and regional change in macrostructure (atrophy and cortical thinning), and microstructure (diffusion metrics). The main outcome was longitudinal effect size (ES) for each outcome. Such ESs can be used to calculate sample-size requirements for clinical trials for hypothesised treatment efficacies. RESULTS: Longitudinal changes in macrostructural neuroimaging measures such as caudate atrophy and ventricular expansion were significantly larger in HD than controls, giving rise to consistently large ES over the 6-month, 9-month and 15-month intervals. Analogous ESs for cortical metrics were smaller with wide CIs. Microstructural (diffusion) neuroimaging metrics ESs were also typically smaller over the shorter intervals, although caudate diffusivity metrics performed strongly over 9 and 15 months. Clinical and cognitive outcomes exhibited small longitudinal ESs, particularly over 6-month and 9-month intervals, with wide CIs, indicating a lack of precision. CONCLUSIONS: To exploit the potential power of specific neuroimaging measures such as caudate atrophy in disease-modifying trials, we propose their use as (1) initial short-term readouts in early phase/proof-of-concept studies over 6 or 9 months, and (2) secondary end points in efficacy studies over longer periods such as 15 months.
Legge RM, Sendi S, Cole JH, et al., 2015, Modulatory effects of brain-derived neurotrophic factor Val66Met polymorphism on prefrontal regions in major depressive disorder, BRITISH JOURNAL OF PSYCHIATRY, Vol: 206, Pages: 379-384, ISSN: 0007-1250
Lorenz R, Monti R, Cole J, et al., 2015, Towards steering the chronnectome - on the potential of dynamic functional connectivity-based neurofeedback of large scale brain networks, Real-time Functional Imaging and Neurofeedback Conference
McColgan P, Seunarine KK, Razi A, et al., 2015, Selective vulnerability of Rich Club brain regions is an organizational principle of structural connectivity loss in Huntington's disease, BRAIN, Vol: 138, ISSN: 0006-8950
Vera J, Winston A, Gunn R, et al., 2015, Microbial translocation is associated with neuroinflammation in HIV subjects on ART, Publisher: WILEY-BLACKWELL, Pages: 6-7, ISSN: 1464-2662
Cole JH, Farmer RE, Rees EM, et al., 2014, Test-Retest Reliability of Diffusion Tensor Imaging in Huntington's Disease., PLoS Curr, Vol: 6
Diffusion tensor imaging (DTI) has shown microstructural abnormalities in patients with Huntington's Disease (HD) and work is underway to characterise how these abnormalities change with disease progression. Using methods that will be applied in longitudinal research, we sought to establish the reliability of DTI in early HD patients and controls. Test-retest reliability, quantified using the intraclass correlation coefficient (ICC), was assessed using region-of-interest (ROI)-based white matter atlas and voxelwise approaches on repeat scan data from 22 participants (10 early HD, 12 controls). T1 data was used to generate further ROIs for analysis in a reduced sample of 18 participants. The results suggest that fractional anisotropy (FA) and other diffusivity metrics are generally highly reliable, with ICCs indicating considerably lower within-subject compared to between-subject variability in both HD patients and controls. Where ICC was low, particularly for the diffusivity measures in the caudate and putamen, this was partly influenced by outliers. The analysis suggests that the specific DTI methods used here are appropriate for cross-sectional research in HD, and give confidence that they can also be applied longitudinally, although this requires further investigation. An important caveat for DTI studies is that test-retest reliability may not be evenly distributed throughout the brain whereby highly anisotropic white matter regions tended to show lower relative within-subject variability than other white or grey matter regions.
Crawford HE, Gregory S, Hobbs NZ, et al., 2014, ASSOCIATION BETWEEN BRAIN VOLUME AND WHITE MATTER MICROSTRUCTURE IN HEALTHY CONTROLS, 8th European-Huntington's-Disease-Network Plenary Meeting, Publisher: BMJ PUBLISHING GROUP, Pages: A39-A40, ISSN: 0022-3050
Rees EM, Farmer R, Cole JH, et al., 2014, Cerebellar abnormalities in Huntington's disease: A role in motor and psychiatric impairment?, Movement Disorders, Vol: 29, Pages: 1648-1654, ISSN: 0885-3185
Rees EM, Farmer R, Cole JH, et al., 2014, Inconsistent emotion recognition deficits across stimulus modalities in Huntington׳s disease., Neuropsychologia, Vol: 64, Pages: 99-104
BACKGROUND: Recognition of negative emotions is impaired in Huntington׳s Disease (HD). It is unclear whether these emotion-specific problems are driven by dissociable cognitive deficits, emotion complexity, test cue difficulty, or visuoperceptual impairments. This study set out to further characterise emotion recognition in HD by comparing patterns of deficits across stimulus modalities; notably including for the first time in HD, the more ecologically and clinically relevant modality of film clips portraying dynamic facial expressions. METHODS: Fifteen early HD and 17 control participants were tested on emotion recognition from static facial photographs, non-verbal vocal expressions and one second dynamic film clips, all depicting different emotions. RESULTS: Statistically significant evidence of impairment of anger, disgust and fear recognition was seen in HD participants compared with healthy controls across multiple stimulus modalities. The extent of the impairment, as measured by the difference in the number of errors made between HD participants and controls, differed according to the combination of emotion and modality (p=0.013, interaction test). The largest between-group difference was seen in the recognition of anger from film clips. CONCLUSIONS: Consistent with previous reports, anger, disgust and fear were the most poorly recognised emotions by the HD group. This impairment did not appear to be due to task demands or expression complexity as the pattern of between-group differences did not correspond to the pattern of errors made by either group; implicating emotion-specific cognitive processing pathology. There was however evidence that the extent of emotion recognition deficits significantly differed between stimulus modalities. The implications in terms of designing future tests of emotion recognition and care giving are discussed.
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