66 results found
Cole JH, Marioni RE, Harris SE, et al., 2019, Brain age and other bodily 'ages': implications for neuropsychiatry., Mol Psychiatry, Vol: 24, Pages: 266-281
As our brains age, we tend to experience cognitive decline and are at greater risk of neurodegenerative disease and dementia. Symptoms of chronic neuropsychiatric diseases are also exacerbated during ageing. However, the ageing process does not affect people uniformly; nor, in fact, does the ageing process appear to be uniform even within an individual. Here, we outline recent neuroimaging research into brain ageing and the use of other bodily ageing biomarkers, including telomere length, the epigenetic clock, and grip strength. Some of these techniques, using statistical approaches, have the ability to predict chronological age in healthy people. Moreover, they are now being applied to neurological and psychiatric disease groups to provide insights into how these diseases interact with the ageing process and to deliver individualised predictions about future brain and body health. We discuss the importance of integrating different types of biological measurements, from both the brain and the rest of the body, to build more comprehensive models of the biological ageing process. Finally, we propose seven steps for the field of brain-ageing research to take in coming years. This will help us reach the long-term goal of developing clinically applicable statistical models of biological processes to measure, track and predict brain and body health in ageing and disease.
Azor AM, Cole JH, Holland AJ, et al., 2019, Increased brain age in adults with Prader-Willi syndrome., Neuroimage Clin, Vol: 21
Prader-Willi syndrome (PWS) is the most common genetic obesity syndrome, with associated learning difficulties, neuroendocrine deficits, and behavioural and psychiatric problems. As the life expectancy of individuals with PWS increases, there is concern that alterations in brain structure associated with the syndrome, as a direct result of absent expression of PWS genes, and its metabolic complications and hormonal deficits, might cause early onset of physiological and brain aging. In this study, a machine learning approach was used to predict brain age based on grey matter (GM) and white matter (WM) maps derived from structural neuroimaging data using T1-weighted magnetic resonance imaging (MRI) scans. Brain-predicted age difference (brain-PAD) scores, calculated as the difference between chronological age and brain-predicted age, are designed to reflect deviations from healthy brain aging, with higher brain-PAD scores indicating premature aging. Two separate adult cohorts underwent brain-predicted age calculation. The main cohort consisted of adults with PWS (n = 20; age mean 23.1 years, range 19.8-27.7; 70.0% male; body mass index (BMI) mean 30.1 kg/m2, 21.5-47.7; n = 19 paternal chromosome 15q11-13 deletion) and age- and sex-matched controls (n = 40; age 22.9 years, 19.6-29.0; 65.0% male; BMI 24.1 kg/m2, 19.2-34.2) adults (BMI PWS vs. control P = .002). Brain-PAD was significantly greater in PWS than controls (effect size mean ± SEM +7.24 ± 2.20 years [95% CI 2.83, 11.63], P = .002). Brain-PAD remained significantly greater in PWS than controls when restricting analysis to a sub-cohort matched for BMI consisting of n = 15 with PWS with BMI range 21.5-33.7 kg/m2, and n = 29 controls with BMI 21.7-34.2 kg/m2 (effect size +5.51 ± 2.56 years [95% CI 3.44, 10.38], P = .037). In the PWS group, brain-PAD scores were not associated with intelligence quotient (IQ), use of hormonal and psychotropic medications, nor severity of repetitive or disruptive
Inkster B, Simmons A, Cole JH, et al., 2018, Unravelling the GSK3 beta-related genotypic interaction network influencing hippocampal volume in recurrent major depressive disorder, PSYCHIATRIC GENETICS, Vol: 28, Pages: 77-84, ISSN: 0955-8829
Cole JH, 2018, Neuroimaging Studies Illustrate the Commonalities Between Ageing and Brain Diseases., Bioessays, Vol: 40
The lack of specificity in neuroimaging studies of neurological and psychiatric diseases suggests that these different diseases have more in common than is generally considered. Potentially, features that are secondary effects of different pathological processes may share common neurobiological underpinnings. Intriguingly, many of these mechanisms are also observed in studies of normal (i.e., non-pathological) brain ageing. Different brain diseases may be causing premature or accelerated ageing to the brain, an idea that is supported by a line of "brain ageing" research that combines neuroimaging data with machine learning analysis. In reviewing this field, I conclude that such observations could have important implications, suggesting that we should shift experimental paradigm: away from characterizing the average case-control brain differences resulting from a disease toward methods that place individuals in their age-appropriate context. This will also lead naturally to clinical applications, whereby neuroimaging can contribute to a personalized-medicine approach to improve brain health.
Cole JH, Caan MWA, Underwood J, et al., 2018, No Evidence for Accelerated Aging-Related Brain Pathology in Treated Human Immunodeficiency Virus: Longitudinal Neuroimaging Results From the Comorbidity in Relation to AIDS (COBRA) Project, CLINICAL INFECTIOUS DISEASES, Vol: 66, Pages: 1899-1909, ISSN: 1058-4838
De Francesco D, Wit FW, Cole JH, et al., 2018, The 'COmorBidity in Relation to AIDS' (COBRA) cohort: Design, methods and participant characteristics, PLOS ONE, Vol: 13, ISSN: 1932-6203
Cole JH, Jolly A, de Simoni S, et al., 2018, Spatial patterns of progressive brain volume loss after moderate-severe traumatic brain injury, BRAIN, Vol: 141, Pages: 822-836, ISSN: 0006-8950
Jenkins PO, De Simoni S, Bourke NJ, et al., 2018, Dopaminergic abnormalities following traumatic brain injury, BRAIN, Vol: 141, Pages: 797-810, ISSN: 0006-8950
Lancaster J, Lorenz R, Leech R, et al., 2018, Bayesian Optimization for Neuroimaging Pre-processing in Brain Age Classification and Prediction, FRONTIERS IN AGING NEUROSCIENCE, Vol: 10, ISSN: 1663-4365
Scott G, Zetterberg H, Jolly A, et al., 2018, Minocycline reduces chronic microglial activation after brain trauma but increases neurodegeneration, BRAIN, Vol: 141, Pages: 459-471, ISSN: 0006-8950
De Simoni S, Jenkins PO, Bourke NJ, et al., 2018, Altered caudate connectivity is associated with executive dysfunction after traumatic brain injury, BRAIN, Vol: 141, Pages: 148-164, ISSN: 0006-8950
van Zoest RA, Underwood J, De Francesco D, et al., 2018, Structural Brain Abnormalities in Successfully Treated HIV Infection: Associations With Disease and Cerebrospinal Fluid Biomarkers, JOURNAL OF INFECTIOUS DISEASES, Vol: 217, Pages: 69-81, ISSN: 0022-1899
Ranlund S, Rosa MJ, de Jong S, et al., 2018, Associations between polygenic risk scores for four psychiatric illnesses and brain structure using multivariate pattern recognition., Neuroimage Clin, Vol: 20, Pages: 1026-1036
Psychiatric illnesses are complex and polygenic. They are associated with widespread alterations in the brain, which are partly influenced by genetic factors. There have been some attempts to relate polygenic risk scores (PRS) - a measure of the overall genetic risk an individual carries for a disorder - to brain structure using univariate methods. However, PRS are likely associated with distributed and covarying effects across the brain. We therefore used multivariate machine learning in this proof-of-principle study to investigate associations between brain structure and PRS for four psychiatric disorders; attention deficit-hyperactivity disorder (ADHD), autism, bipolar disorder and schizophrenia. The sample included 213 individuals comprising patients with depression (69), bipolar disorder (33), and healthy controls (111). The five psychiatric PRSs were calculated based on summary data from the Psychiatric Genomics Consortium. T1-weighted magnetic resonance images were obtained and voxel-based morphometry was implemented in SPM12. Multivariate relevance vector regression was implemented in the Pattern Recognition for Neuroimaging Toolbox (PRoNTo). Across the whole sample, a multivariate pattern of grey matter significantly predicted the PRS for autism (r = 0.20, pFDR = 0.03; MSE = 4.20 × 10-5, pFDR = 0.02). For the schizophrenia PRS, the MSE was significant (MSE = 1.30 × 10-5, pFDR = 0.02) although the correlation was not (r = 0.15, pFDR = 0.06). These results lend support to the hypothesis that polygenic liability for autism and schizophrenia is associated with widespread changes in grey matter concentrations. These associations were seen in individuals not affected by these disorders, indicating that this is not driven by the expression of the disease, but by the genetic risk captured by the PRSs.
Cole JH, Poudel RPK, Tsagkrasoulis D, et al., 2017, Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker, NEUROIMAGE, Vol: 163, Pages: 115-124, ISSN: 1053-8119
Cole JH, Franke K, 2017, Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers, Trends in Neurosciences, Vol: 40, Pages: 681-690, ISSN: 0166-2236
The brain changes as we age and these changes are associated with functional deterioration and neurodegenerative disease. It is vital that we better understand individual differences in the brain ageing process; hence, techniques for making individualised predictions of brain ageing have been developed. We present evidence supporting the use of neuroimaging-based ‘brain age’ as a biomarker of an individual's brain health. Increasingly, research is showing how brain disease or poor physical health negatively impacts brain age. Importantly, recent evidence shows that having an ‘older’-appearing brain relates to advanced physiological and cognitive ageing and the risk of mortality. We discuss controversies surrounding brain age and highlight emerging trends such as the use of multimodality neuroimaging and the employment of ‘deep learning’ methods. Brain age can be predicted in individuals based on neuroimaging data using machine learning approaches to model trajectories of healthy brain ageing. The predicted brain age for a new individual can differ from his or her chronological age; this difference appears to reflect advanced or delayed brain ageing. Brain age has been shown to relate to cognitive ageing and multiple aspects of physiological ageing and to predict the risk of neurodegenerative diseases and mortality in older adults. Various diseases, including HIV, schizophrenia, and diabetes, have been shown to make the brain appear older. Further, brain age is being used to identify possible protective or deleterious factors for brain health as people age. Brain age is being actively developed to combine multiple measures of brain structure and function, capturing increasing amounts of detail on the ageing brain.
Wise T, Radua J, Via E, et al., 2017, Common and distinct patterns of grey-matter volume alteration in major depression and bipolar disorder: evidence from voxel-based meta-analysis, MOLECULAR PSYCHIATRY, Vol: 22, Pages: 1455-1463, ISSN: 1359-4184
Inkster B, Simmons A, Cole J, et al., 2017, GSK3 beta: A PLAUSIBLE MEDIATOR OF HIPPOCAMPAL CHANGE INDUCED BY ERYTHROPOIETIN TREATMENT IN DEPRESSION, 23rd Annual World Congress of Psychiatric Genetics (WCPG), Publisher: ELSEVIER SCIENCE BV, Pages: S214-S214, ISSN: 0924-977X
Cole JH, 2017, Neuroimaging-derived brain-age: an ageing biomarker?, AGING-US, Vol: 9, Pages: 1861-1862, ISSN: 1945-4589
Cole JH, Annus T, Wilson LR, et al., 2017, Brain-predicted age in Down syndrome is associated with beta amyloid deposition and cognitive decline, NEUROBIOLOGY OF AGING, Vol: 56, Pages: 41-49, ISSN: 0197-4580
Underwood J, Cole JH, Caan M, et al., 2017, Gray and White Matter Abnormalities in Treated Human Immunodeficiency Virus Disease and Their Relationship to Cognitive Function, CLINICAL INFECTIOUS DISEASES, Vol: 65, Pages: 422-432, ISSN: 1058-4838
Feeney C, Sharp DJ, Hellyer PJ, et al., 2017, Serum Insulin-like Growth Factor-I Levels are Associated with Improved White Matter Recovery after Traumatic Brain Injury, ANNALS OF NEUROLOGY, Vol: 82, Pages: 30-43, ISSN: 0364-5134
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
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
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
Zhu D, Riedel BC, Jahanshad N, et al., 2017, Classification of major depressive disorder via multi-site weighted LASSO model, Pages: 159-167, ISSN: 0302-9743
© Springer International Publishing AG 2017. Large-scale collaborative analysis of brain imaging data, in psychiatry and neurology, offers a new source of statistical power to discover features that boost accuracy in disease classification, differential diagnosis, and outcome prediction. However, due to data privacy regulations or limited accessibility to large datasets across the world, it is challenging to efficiently integrate distributed information. Here we propose a novel classification framework through multi-site weighted LASSO: each site performs an iterative weighted LASSO for feature selection separately. Within each iteration, the classification result and the selected features are collected to update the weighting parameters for each feature. This new weight is used to guide the LASSO process at the next iteration. Only the features that help to improve the classification accuracy are preserved. In tests on data from five sites (299 patients with major depressive disorder (MDD) and 258 normal controls), our method boosted classification accuracy for MDD by 4.9% on average. This result shows the potential of the proposed new strategy as an effective and practical collaborative platform for machine learning on large scale distributed imaging and biobank data.
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
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
Schouten J, Su T, Wit FW, et al., 2016, Determinants of reduced cognitive performance in HIV-1-infected middle-aged men on combination antiretroviral therapy, AIDS, Vol: 30, Pages: 1027-1038, ISSN: 0269-9370
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