Imperial College London

DrJamesCole

Faculty of MedicineDepartment of Medicine

Honorary Research Fellow
 
 
 
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james.cole

 
 
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C3NLBurlington DanesHammersmith Campus

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Publications

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69 results found

Cole JH, Marioni RE, Harris SE, Deary IJet 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.

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Azor AM, Cole JH, Holland AJ, Dumba M, Patel MC, Sadlon A, Goldstone AP, Manning KEet al., 2019, Increased brain age in adults with Prader-Willi syndrome, NeuroImage: Clinical, ISSN: 2213-1582

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

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Tozzi L, Garczarek L, Janowitz D, Stein DJ, Wittfeld K, Dobrowolny H, Lagopoulos J, Hatton SN, Hickie IB, Carballedo A, Brooks SJ, Vuletic D, Uhlmann A, Veer IM, Walter H, Bülow R, Völzke H, Klinger-König J, Schnell K, Schoepf D, Grotegerd D, Opel N, Dannlowski U, Kugel H, Schramm E, Konrad C, Kircher T, Jüksel D, Nenadic I, Krug A, Hahn T, Steinsträter O, Redlich R, Zaremba D, Zurowski B, Fu CHY, DIma D, Cole J, Grabe HJ, Connolly CG, Yang TT, Ho TC, Lewinn KZ, Li M, Groenewold NA, Salminen LE, Walter M, Simmons AN, Van Erp TGM, Jahanshad N, Baune BT, Van Der Wee NJA, Van Tol MJ, Penninx BWJH, Hibar DP, Thompson PM, Veltman DJ, Schmaal L, Frodl Tet al., 2019, Interactive impact of childhood maltreatment, depression, and age on cortical brain structure: Mega-analytic findings from a large multi-site cohort, Psychological Medicine, ISSN: 0033-2917

© 2019 Cambridge University Press. Background. Childhood maltreatment (CM) plays an important role in the development of major depressive disorder (MDD). The aim of this study was to examine whether CM severity and type are associated with MDD-related brain alterations, and how they interact with sex and age.MethodsWithin the ENIGMA-MDD network, severity and subtypes of CM using the Childhood Trauma Questionnaire were assessed and structural magnetic resonance imaging data from patients with MDD and healthy controls were analyzed in a mega-analysis comprising a total of 3872 participants aged between 13 and 89 years. Cortical thickness and surface area were extracted at each site using FreeSurfer.ResultsCM severity was associated with reduced cortical thickness in the banks of the superior temporal sulcus and supramarginal gyrus as well as with reduced surface area of the middle temporal lobe. Participants reporting both childhood neglect and abuse had a lower cortical thickness in the inferior parietal lobe, middle temporal lobe, and precuneus compared to participants not exposed to CM. In males only, regardless of diagnosis, CM severity was associated with higher cortical thickness of the rostral anterior cingulate cortex. Finally, a significant interaction between CM and age in predicting thickness was seen across several prefrontal, temporal, and temporo-parietal regions.ConclusionsSeverity and type of CM may impact cortical thickness and surface area. Importantly, CM may influence age-dependent brain maturation, particularly in regions related to the default mode network, perception, and theory of mind.

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Cole JH, Lorenz R, Geranmayeh F, Wood T, Hellyer P, Williams S, Turkheimer F, Leech Ret al., 2018, Active Acquisition for multimodal neuroimaging, Wellcome Open Research, Vol: 3, Pages: 145-145

<ns4:p>In many clinical and scientific situations the optimal neuroimaging sequence may not be known prior to scanning and may differ for each individual being scanned, depending on the exact nature and location of abnormalities. Despite this, the standard approach to data acquisition, in such situations, is to specify the sequence of neuroimaging scans prior to data acquisition and to apply the same scans to all individuals. In this paper, we propose and illustrate an alternative approach, in which data would be analysed as it is acquired and used to choose the future scanning sequence: Active Acquisition. We propose three Active Acquisition scenarios based around multiple MRI modalities. In Scenario 1, we propose a simple use of near-real time analysis to decide whether to acquire more or higher resolution data, or acquire data with a different field<ns4:bold>-</ns4:bold>of<ns4:bold>-</ns4:bold>view. In Scenario 2, we simulate how multimodal MR data could be actively acquired and combined with a decision tree to classify a known outcome variable (in the simple example here, age). In Scenario 3, we simulate using Bayesian optimisation to actively search across multiple MRI modalities to find those which are most abnormal. These simulations suggest that by actively acquiring data, the scanning sequence can be adapted to each individual. We also consider the many outstanding practical and technical challenges involving normative data acquisition, MR physics, statistical modelling and clinical relevance. Despite these, we argue that  Active Acquisition allows for potentially far more powerful, sensitive or rapid data acquisition, and may open up different perspectives on individual differences, clinical conditions, and biomarker discovery.</ns4:p>

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Inkster B, Simmons A, Cole JH, Schoof E, Linding R, Nichols T, Muglia P, Holsboer F, Saemann PG, McGuffin P, Fu CHY, Miskowiak K, Matthews PM, Zai G, Nicodemus Ket 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

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Underwood J, Cole JH, Leech R, Sharp DJ, Winston Aet al., 2018, Multivariate pattern analysis of volumetric neuroimaging data and its relationship with cognitive function in treated HIV-disease, Journal of Acquired Immune Deficiency Syndromes, Vol: 78, Pages: 429-436, ISSN: 1525-4135

BACKGROUND: Accurate prediction of longitudinal changes in cognitive function would potentially allow targeted intervention in those at greatest risk of cognitive decline. We sought to build a multivariate model using volumetric neuroimaging data alone to accurately predict cognitive function. METHODS: Volumetric T1-weighted neuroimaging data from virally suppressed HIV-positive individuals from the CHARTER cohort (n=139) were segmented into grey and white matter and spatially normalised before were entering into machine learning models. Prediction of cognitive function at baseline and longitudinally was determined using leave-one-out cross validation. Additionally, a multivariate model of brain ageing was used to measure the deviation of apparent brain age from chronological age and assess its relationship with cognitive function. RESULTS: Cognitive impairment, defined using the global deficit score, was present in 37.4%. However, it was generally mild and occurred more commonly in those with confounding comorbidities (p<0.001). Although multivariate prediction of cognitive impairment as a dichotomous variable at baseline was poor (AUC 0.59), prediction of the global T-score was better than a comparable linear model (adjusted R=0.08, p<0.01 vs. adjusted R=0.01, p=0.14). Accurate prediction of longitudinal changes in cognitive function was not possible (p=0.82).Brain-predicted age exceeded chronological age by mean (95% confidence interval) 1.17 (-0.14-2.53) years, but was greatest in those with confounding comorbidities (5.87 [1.74-9.99] years) and prior AIDS (3.03 [0.00-6.06] years). CONCLUSION: Accurate prediction of cognitive impairment using multivariate models using only T1-weighted data was not achievable, which may reflect the small sample size, heterogeneity of the data or that impairment was usually mild.

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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.

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Cole JH, caan M, Underwood J, de Francesco D, van Zoest R, Wit F, Mutsaerts H, Leech R, Geurtsen G, Portigies P, Majoie C, Schim van der Loeff M, Sabin C, Reiss P, Winston A, Sharp Det al., 2018, No evidence for accelerated ageing-related brain pathology in treated HIV: longitudinal neuroimaging results from the Comorbidity in Relation to AIDS (COBRA) project, Clinical Infectious Diseases, Vol: 66, Pages: 1899-1909, ISSN: 1058-4838

BackgroundDespite successful antiretroviral therapy people living with HIV (PLWH) experience higher rates of age-related morbidity, including abnormal brain structure, brain function and cognitive impairment. This has raised concerns that PLWH may experience accelerated ageing-related brain pathology.MethodsWe performed a multi-centre longitudinal study of 134 virologically-suppressed PLWH (median age = 56.0 years) and 79 demographically-similar HIV-negative controls (median age = 57.2 years). To measure cognitive performance and brain pathology, we conducted detailed neuropsychological assessments and multi-modality neuroimaging (T1-weighted, T2-weighted, diffusion-MRI, resting-state functional-MRI, spectroscopy, arterial spin labelling) at baseline and after two-year follow-up. Group differences in rates of change were assessed using linear mixed effects models.Results123 PLWH and 78 HIV-negative controls completed longitudinal assessments (median interval = 1.97 years). There were no differences between PLWH and HIV-negative controls in age, sex, years of education, smoking, alcohol use, recreational drug use, blood pressure, body-mass index or cholesterol levels.At baseline, PLWH had poorer global cognitive performance (P<0.01), lower grey matter volume (P=0.04), higher white matter hyperintensity load (P=0.02), abnormal white-matter microstructure (P<0.005) and greater ‘brain-predicted age difference’ (P=0.01). Longitudinally, there were no significant differences in rates of change in any neuroimaging measure between PLWH and HIV-negative controls (P>0.1). Cognitive performance was stable across the study period in both groups.ConclusionsOur finding indicate that when receiving successful treatment, middle-aged PLWH are not at increased risk of accelerated ageing-related brain changes or cognitive decline over two years, when compared to closely-matched HIV-negative controls.

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De Francesco D, Wit FW, Cole JH, Kootstra NA, Winston A, Sabin CA, Underwood J, van Zoest RA, Schouten J, Kooij KW, Prins M, Guaraldi G, Caan MWA, Burger D, Franceschi C, Libert C, Bürkle A, Reiss P, COmorBidity in Relation to AIDS COBRA collaborationet al., 2018, The 'COmorBidity in Relation to AIDS' (COBRA) cohort: Design, methods and participant characteristics, PLoS ONE, Vol: 13, ISSN: 1932-6203

BACKGROUND: Persons living with HIV on combination antiretroviral therapy (cART) may be at increased risk of the development of age-associated non-communicable comorbidities (AANCC) at relatively young age. It has therefore been hypothesised that such individuals, despite effective cART, may be prone to accelerated aging. OBJECTIVE: The COmorBidity in Relation to AIDS (COBRA) cohort study was designed to investigate the potential causal link between HIV and AANCC, amongst others, in a cohort of middle-aged individuals with HIV with sustained viral suppression on cART and otherwise comparable HIV-negative controls. METHODS: Longitudinal cohort study of HIV-positive subjects ≥45 years of age, with sustained HIV suppression on cART recruited from two large European HIV treatment centres and similarly-aged HIV-negative controls recruited from sexual health centres and targeted community groups. Both HIV-positive and HIV-negative subjects were assessed at study entry and again at follow-up after 2 years. RESULTS: Of the 134 HIV-positive individuals with a median (IQR) age of 56 (51, 62) years recruited, 93% were male, 88% of white ethnicity and 86% were men who have sex with men (MSM). Similarly, the 79 HIV-negative subjects had a median (IQR) age of 57 (52, 64) and 92% were male, 97% of white ethnicity and 80% were MSM. CONCLUSIONS: The results from the COBRA study will be a significant resource to understand the link between HIV and AANCC and the pathogenic mechanisms underlying this link. COBRA will inform future development of novel prognostic tools for earlier diagnosis of AANCC and of novel interventions which, as an adjunct to cART, may prevent AANCC.

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Lancaster J, Lorenz R, Leech R, Cole JHet al., 2018, Bayesian Optimisation for Neuroimaging Pre-processing in Brain Age Classification and Prediction, Frontiers in Aging Neuroscience, Vol: 10, ISSN: 1663-4365

Neuroimaging-based age prediction using machine learning is proposed as a biomarker of brain aging, relating to cognitive performance, health outcomes and progression of neurodegenerative disease. However, even leading age-prediction algorithms contain measurement error, motivating efforts to improve experimental pipelines. T1-weighted MRI is commonly used for age prediction, and the pre-processing of these scans involves normalization to a common template and resampling to a common voxel size, followed by spatial smoothing. Resampling parameters are often selected arbitrarily. Here, we sought to improve brain-age prediction accuracy by optimizing resampling parameters using Bayesian optimization. Using data on N = 2003 healthy individuals (aged 16–90 years) we trained support vector machines to (i) distinguish between young (<22 years) and old (>50 years) brains (classification) and (ii) predict chronological age (regression). We also evaluated generalisability of the age-regression model to an independent dataset (CamCAN, N = 648, aged 18–88 years). Bayesian optimization was used to identify optimal voxel size and smoothing kernel size for each task. This procedure adaptively samples the parameter space to evaluate accuracy across a range of possible parameters, using independent sub-samples to iteratively assess different parameter combinations to arrive at optimal values. When distinguishing between young and old brains a classification accuracy of 88.1% was achieved, (optimal voxel size = 11.5 mm3, smoothing kernel = 2.3 mm). For predicting chronological age, a mean absolute error (MAE) of 5.08 years was achieved, (optimal voxel size = 3.73 mm3, smoothing kernel = 3.68 mm). This was compared to performance using default values of 1.5 mm3 and 4mm respectively, resulting in MAE = 5.48 years, though this 7.3% improvement was not statistically significant. When assessing generalisability, best performance was achieved when applying the entire Baye

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Jenkins PO, De Simoni S, Bourke N, Fleminger J, Scott G, Towey D, Svensson W, Khan S, Patel M, Greenwood R, Cole J, Sharp DJet al., 2018, Dopaminergic abnormalities following traumatic brain injury, Brain, Vol: 141, Pages: 797-810, ISSN: 1460-2156

Traumatic brain injury can reduce striatal dopamine levels. The cause of this is uncertain, but is likely to be related to damage to the nigrostriatal system. We investigated the pattern of striatal dopamine abnormalities using 123I-Ioflupane single-photon emission computed tomography (SPECT) scans and their relationship to nigrostriatal damage and clinical features. We studied 42 moderate–severe traumatic brain injury patients with cognitive impairments but no motor parkinsonism signs and 20 healthy controls. 123I-Ioflupane scanning was used to assess dopamine transporter levels. Clinical scan reports were compared to quantitative dopamine transporter results. Advanced MRI methods were used to assess the nigrostriatal system, including the area through which the nigrostriatal projections pass as defined from high-resolution Human Connectome data. Detailed clinical and neuropsychological assessments were performed. Around 20% of our moderate–severe patients had clear evidence of reduced specific binding ratios for the dopamine transporter in the striatum measured using 123I-Ioflupane SPECT. The caudate was affected more consistently than other striatal regions. Dopamine transporter abnormalities were associated with reduced substantia nigra volume. In addition, diffusion MRI provided evidence of damage to the regions through which the nigrostriatal tract passes, particularly the area traversed by dopaminergic projections to the caudate. Only a small percentage of patients had evidence of macroscopic lesions in the striatum and there was no relationship between presence of lesions and dopamine transporter specific binding ratio abnormalities. There was also no relationship between reduced volume in the striatal subregions and reduced dopamine transporter specific binding ratios. Patients with low caudate dopamine transporter specific binding ratios show impaired processing speed and executive dysfunction compared to patients with normal levels. Taken toge

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Cole JH, Jolly A, De Simoni S, Bourke N, patel M, Scott G, Sharp Det al., 2018, Spatial patterns of progressive brain volume loss after moderate-severe traumatic brain injury, Brain, Vol: 141, Pages: 822-836, ISSN: 1460-2156

Traumatic brain injury leads to significant loss of brain volume, which continues into the chronic stage. This can be sensitively measured using volumetric analysis of magnetic resonance imaging. Here we: (i) investigated longitudinal patterns of brain atrophy; (ii) tested whether atrophy is greatest in sulcal cortical regions, and (iii) showed how atrophy could be used to power intervention trials aimed at slowing neurodegeneration. In 61 moderate/severe traumatic brain injury patients (mean age = 41.55 years ± 12.77) and 32 healthy controls (mean age = 34.22 years ± 10.29), cross-sectional and longitudinal (one-year follow-up) brain structure was assessed using voxel-based morphometry on T1-weighted scans. Longitudinal brain volume changes were characterised using a novel neuroimaging analysis pipeline that generates a Jacobian determinant metric, reflecting spatial warping between baseline and follow-up scans. Jacobian determinant values were summarised regionally and compared with clinical and neuropsychological measures. Traumatic brain injury patients showed lower grey and white matter volume in multiple brain regions compared to controls at baseline. Atrophy over one year was pronounced following traumatic brain injury. Traumatic brain injury patients lost a mean (± standard deviation) of 1.55% ± 2.19 of grey matter volume per year, 1.49% ± 2.20 of white matter volume or 1.51% ± 1.60 of whole brain volume. Healthy controls lost 0.55% ± 1.13 of grey matter volume and gained 0.26% ± 1.11 of white matter volume; equating to a 0.22% ± 0.83 reduction in whole brain volume. Atrophy was greatest in white matter, where the majority (84%) of regions were affected. This effect was independent of and substantially greater than that of ageing. Increased atrophy was also seen in cortical sulci compared to gyri. There was no relationship between atrophy and time since injury or age at baseline. Atrophy rates we

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Ranlund S, Rosa MJ, de Jong S, Cole JH, Kyriakopoulos M, Fu CHY, Mehta MA, Dima Det 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.

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Scott GPT, Zetterberg H, Jolly A, Cole JH, De Simoni S, Jenkins PO, Feeney C, Owen DR, Lingford-Hughes A, Howes O, Patel MC, Goldstone AP, Gunn RN, Blennow K, Matthews PM, Sharp DJet al., 2017, Minocycline reduces chronic microglial activation after brain trauma but increases neurodegeneration, Brain, Vol: 141, Pages: 459-471, ISSN: 1460-2156

Survivors of a traumatic brain injury can deteriorate years later, developing brain atrophy and dementia. Traumatic brain injury triggers chronic microglial activation, but it is unclear whether this is harmful or beneficial. A successful chronic-phase treatment for traumatic brain injury might be to target microglia. In experimental models, the antibiotic minocycline inhibits microglial activation. We investigated the effect of minocycline on microglial activation and neurodegeneration using PET, MRI, and measurement of the axonal protein neurofilament light in plasma. Microglial activation was assessed using 11C-PBR28 PET. The relationships of microglial activation to measures of brain injury, and the effects of minocycline on disease progression, were assessed using structural and diffusion MRI, plasma neurofilament light, and cognitive assessment. Fifteen patients at least 6 months after a moderate-to-severe traumatic brain injury received either minocycline 100 mg orally twice daily or no drug, for 12 weeks. At baseline, 11C-PBR28 binding in patients was increased compared to controls in cerebral white matter and thalamus, and plasma neurofilament light levels were elevated. MRI measures of white matter damage were highest in areas of greater 11C-PBR28 binding. Minocycline reduced 11C-PBR28 binding (mean Δwhite matter binding = −23.30%, 95% confidence interval −40.9 to −5.64%, P = 0.018), but increased plasma neurofilament light levels. Faster rates of brain atrophy were found in patients with higher baseline neurofilament light levels. In this experimental medicine study, minocycline after traumatic brain injury reduced chronic microglial activation while increasing a marker of neurodegeneration. These findings suggest that microglial activation has a reparative effect in the chronic phase of traumatic brain injury.

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De Simoni S, Jenkins PO, Bourke N, Fleminger JJ, Hellyer PJ, Jolly AE, Patel MC, Cole J, Leech R, Sharp DJet al., 2017, Altered caudate connectivity is associated with executive dysfunction after traumatic brain injury, Brain, Vol: 141, Pages: 148-164, ISSN: 1460-2156

Traumatic brain injury often produces executive dysfunction. This characteristic cognitive impairment often causes long-term problems with behaviour and personality. Frontal lobe injuries are associated with executive dysfunction, but it is unclear how these injuries relate to corticostriatal interactions that are known to play an important role in behavioural control. We hypothesized that executive dysfunction after traumatic brain injury would be associated with abnormal corticostriatal interactions, a question that has not previously been investigated. We used structural and functional MRI measures of connectivity to investigate this. Corticostriatal functional connectivity in healthy individuals was initially defined using a data-driven approach. A constrained independent component analysis approach was applied in 100 healthy adult dataset from the Human Connectome Project. Diffusion tractography was also performed to generate white matter tracts. The output of this analysis was used to compare corticostriatal functional connectivity and structural integrity between groups of 42 patients with traumatic brain injury and 21 age-matched controls. Subdivisions of the caudate and putamen had distinct patterns of functional connectivity. Traumatic brain injury patients showed disruption to functional connectivity between the caudate and a distributed set of cortical regions, including the anterior cingulate cortex. Cognitive impairments in the patients were mainly seen in processing speed and executive function, as well as increased levels of apathy and fatigue. Abnormalities of caudate functional connectivity correlated with these cognitive impairments, with reductions in right caudate connectivity associated with increased executive dysfunction, information processing speed and memory impairment. Structural connectivity, measured using diffusion tensor imaging between the caudate and anterior cingulate cortex was impaired and this also correlated with measures of ex

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Van Zoest RA, Underwood J, De Francesco D, Sabin CA, Cole JH, Wit FW, Caan MWA, Kootstra NA, Fuchs D, Zetterberg H, Majoie CBLM, Portegies P, Winston A, Sharp DJ, Gisslén M, Reiss P, Co-morBidity in Relation to AIDS COBRA Collaborationet al., 2017, 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

Background: Brain structural abnormalities have been reported in persons with HIV (PWH) on suppressive combination antiretroviral therapy (cART), but their pathophysiology remains unclear. Methods: We investigated factors associated with brain tissue volumes and white matter microstructure (fractional anisotropy) in 134 PWH on suppressive cART and 79 comparable HIV-negative controls, aged ≥45 years from the Co-morBidity in Relation to AIDS (COBRA) cohort, using multimodal neuroimaging and cerebrospinal fluid (CSF) biomarkers. Results: Compared to controls, PWH had lower grey matter volumes (-13.7 mL [95%-confidence interval -25.1, -2.2 mL]) and fractional anisotropy (-0.0073 [-0.012, -0.0024]), with the largest differences observed in those with prior clinical AIDS. Hypertension and CSF soluble CD14 concentration were associated with lower fractional anisotropy. These associations were independent of HIV serostatus (Pinteraction=0.32 and Pinteraction=0.59, respectively) and did not explain the greater abnormalities in brain structure in relation to HIV. Conclusions: The presence of lower grey matter volumes and more white matter microstructural abnormalities in well-treated PWH partly reflect a combination of historical effects of AIDS, as well as the more general influence of systemic factors such as hypertension and ongoing neuroinflammation. Additional mechanisms explaining the accentuation of brain structure abnormalities in treated HIV infection remain to be identified.

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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.

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Inkster B, Simmons A, Cole J, Schoof E, Linding R, Nichols T, Muglia P, Saemann P, McGuffin P, Fu C, Zai G, Miskowiak K, Matthews P, Nicodemus Ket 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

CONFERENCE PAPER

Cole JH, 2017, Neuroimaging-derived brain-age: an ageing biomarker?, AGING-US, Vol: 9, Pages: 1861-1862, ISSN: 1945-4589

JOURNAL ARTICLE

Cole JH, Poudel RPK, Tsagkrasoulis D, Caan MWA, Steves C, Spector TD, Montana Get 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

Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people. Deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the credentials of 'brain-predicted age' as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre-processed and raw T1-weighted MRI data. Firstly, we aimed to demonstrate the accuracy of CNN brain-predicted age using a large dataset of healthy adults (N = 2001). Next, we sought to establish the heritability of brain-predicted age using a sample of monozygotic and dizygotic female twins (N = 62). Thirdly, we examined the test-retest and multi-centre reliability of brain-predicted age using two samples (within-scanner N = 20; between-scanner N = 11). CNN brain-predicted ages were generated and compared to a Gaussian Process Regression (GPR) approach, on all datasets. Input data were grey matter (GM) or white matter (WM) volumetric maps generated by Statistical Parametric Mapping (SPM) or raw data. CNN accurately predicted chronological age using GM (correlation between brain-predicted age and chronological age r = 0.96, mean absolute error [MAE] = 4.16 years) and raw (r = 0.94, MAE = 4.65 years) data. This was comparable to GPR brain-predicted age using GM data (r = 0.95, MAE = 4.66 years). Brain-predicted age was a heritable phenotype for all models and input data (h(2) ≥ 0.5). Brain-predicted age showed high test-retest reliability (intraclass correlation coefficient [ICC] = 0.90-0.99). Multi-centre reliability was more variable within high ICCs for GM (0.83-0.96) and poor-moderate levels for WM and raw data (0.51-0.77). Brain-predicted age represents an accur

JOURNAL ARTICLE

Feeney C, Sharp DJ, Hellyer PJ, Jolly AE, Cole JH, Scott G, Baxter D, Jilka S, Ross E, Ham TE, Jenkins PO, Li LM, Gorgoraptis N, Midwinter M, Goldstone APet al., 2017, Serum IGF-I levels are associated with improved white matter recovery after TBI., Annals of Neurology, Vol: 82, Pages: 30-43, ISSN: 0364-5134

OBJECTIVE: Traumatic brain injury (TBI) is a common disabling condition with limited treatment options. Diffusion tensor imaging (DTI) measures recovery of axonal injury in white matter (WM) tracts after TBI. Growth hormone deficiency (GHD) after TBI may impair axonal and neuropsychological recovery, and serum IGF-I may mediate this effect. We conducted a longitudinal study to determine the effects of baseline serum IGF-I concentrations on WM tract and neuropsychological recovery after TBI. METHODS: Thirty-nine adults after TBI (84.6% male; age median 30.5y; 87.2% moderate-severe; time since TBI median 16.3 months, n=4 with GHD) were scanned twice, 13.3 months (12.1-14.9) apart, and 35 healthy controls scanned once. Symptom and quality of life questionnaires and cognitive assessments were completed at both visits (n=33). Our main outcome measure was fractional anisotropy (FA), a measure of WM tract integrity, in a priori regions of interest: splenium of corpus callosum (SPCC), and posterior limb of internal capsule (PLIC). RESULTS: At baseline, FA was reduced in many WM tracts including SPCC and PLIC following TBI compared to controls, indicating axonal injury, with longitudinal increases indicating axonal recovery. There was a significantly greater increase in SPCC FA over time in patients with serum IGF-I above vs. below the median-for-age. Only the higher IGF-I group had significant improvements in immediate verbal memory recall over time. INTERPRETATION: WM recovery and memory improvements after TBI were greater in patients with higher serum IGF-I at baseline. These findings suggest that GH/IGF-I system may be a potential therapeutic target following TBI. This article is protected by copyright. All rights reserved.

JOURNAL ARTICLE

Cole JH, Ritchie SJ, Bastin ME, Valdes Hernandez MC, Munoz Maniega S, Royle N, Corely J, Pattie A, Harris SE, Zhang Q, Wray N, Redmond P, Marioni RE, Starr JM, Cox SR, Wardlaw JM, Sharp DJ, Deary IJet al., 2017, Brain age predicts mortality, Molecular Psychiatry, Vol: 23, Pages: 1385-1392, ISSN: 1476-5578

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.

JOURNAL ARTICLE

Cole JH, Annus T, Wilson LR, Remtulla R, Hong YT, Fryer TD, Acosta-Cabronero J, Cardenas-Blanco A, Smith R, Menon DK, Zaman SH, Nestor PJ, Holland AJet al., 2017, Brain-predicted age in Down Syndrome is associated with β-amyloid deposition and cognitive decline, Neurobiology of Aging, Vol: 56, Pages: 41-49, ISSN: 1558-1497

Individuals with Down Syndrome (DS) are more likely to experience earlier onset of multiple facets of physiological ageing. This includes brain atrophy, β-amyloid deposition, cognitive decline and Alzheimer’s Disease; factors indicative of brain ageing. 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 [11C]-PiB positron emission tomography (PET) scans to index levels of cerebral β-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 ageing, and that age-related alterations in brain structure are associated with individual differences in the rate of β-amyloid deposition and cognitive impairment.

JOURNAL ARTICLE

Underwood J, Cole JH, Caan M, De Francesco D, Leech R, van Zoest RA, Su T, Geurtsen GJ, Schmand BA, Portegies P, Prins M, Wit FW, Sabin CA, Majoie C, Reiss P, Winston A, Sharp DJ, Co-morBidity in Relation to Aids COBRA Collaborationet al., 2017, Grey and white matter abnormalities in treated HIV-disease and their relationship to cognitive function., Clinical Infectious Diseases, Vol: 65, Pages: 422-432, ISSN: 1537-6591

Background: Long-term comorbidities such as cognitive impairment remain prevalent in otherwise effectively treated people-living-with-HIV. We investigate the relationship between cognitive impairment and brain structure in successfully treated patients using multi-modal neuroimaging from the Co-morBidity in Relation to AIDS (COBRA) cohort. Methods: Cognitive function, brain tissue volumes and white matter microstructure were assessed in 134 HIV-positive patients and 79 controls. All patients had suppressed plasma HIV RNA at cohort entry. In addition to comprehensive voxelwise analyses of volumetric and diffusion tensor imaging, we used an unsupervised machine learning approach to combine cognitive, diffusion and volumetric data, taking advantage of the complementary information they provide. Results: Compared to the highly comparable control group, cognitive function was impaired in four out of the six cognitive domains tested (median global T-scores: 50.8 vs. 54.2, p<0.001). Patients had lower grey but not white matter volumes, observed principally in regions where structure generally did not correlate with cognitive function. Widespread abnormalities in white matter microstructure were also seen, including reduced fractional anisotropy with increased mean and radial diffusivity. In contrast to the grey matter, these diffusion abnormalities correlated with cognitive function. Multivariate neuroimaging analysis identified a neuroimaging phenotype associated with poorer cognitive function, HIV-infection and systemic immune activation. Conclusions: Cognitive impairment, lower grey matter volume and white matter microstructural abnormalities were evident in HIV-positive individuals despite fully suppressive antiretroviral therapy. White matter abnormalities appear to be a particularly important determinant of cognitive dysfunction seen in well-treated HIV-positive individuals.

JOURNAL ARTICLE

Pardoe HR, Cole JH, Blackmon K, Thesen T, Kuzniecky Ret al., 2017, Structural brain changes in medically refractory focal epilepsy resemble premature brain aging, Epilepsy Research, Vol: 133, Pages: 28-32, ISSN: 0920-1211

Objective: We used whole brain T1-weighted MRIto estimate the age of individuals with medically refractoryfocal epilepsy, and compared with individuals with newly diagnosed focal epilepsy and healthycontrols. The difference between neuroanatomical age and chronological age was compared betweenthe three groups.Methods: Neuroanatomical age was estimated using a machine learning-based method that was trainedusing structural MRI scans from a large independent healthy control sample (N = 2001). The predictionmodel was then used to estimate age from MRI scans obtained from newly diagnosed focal epilepsypatients (N = 42), medically refractory focal epilepsy patients (N = 94) and healthy controls (N = 74).Results: Individuals with medically refractory epilepsy had a difference between predicted brain age andchronological age that was on average 4.5 years older than healthy controls (p = 4.6 × 10−5). No significantdifferences were observed in newly diagnosed focal epilepsy. Earlier age of onset was associated with anincreased brain age difference in the medically refractory group (p = 0.034).Significance: Medically refractory focal epilepsy is associated with structural brain changes that resemblepremature brain aging.

JOURNAL ARTICLE

Cole JH, underwood J, Caan MWA, De Francesco D, van Zoest RA, Leech R, Wit FWNM, Portegies P, Geurtsen GJ, Schmand BA, Schim van der Loeff MF, Franceschi C, Sabin CA, Majoie CBLM, Winston A, Reiss P, Sharp DJet al., 2017, Increased brain-predicted ageing in treated HIV disease, Neurology, Vol: 88, Pages: 1349-1357, ISSN: 0028-3878

Objective: To establish whether HIV disease is associated with abnormal levels of age-related brain atrophy, by estimating apparent “brain age” using neuroimaging and exploring whether these estimates related to HIV-status, age, cognitive performance and HIV-related clinical parameters.Methods: A large sample of virologically-suppressed HIV-positive adults (N = 162, aged 45-82 years) and highly-comparable HIV-negative controls (N = 105) were recruited as part of the COBRA collaboration. Using T1-MRI scans, a machine-learning model of healthy brain ageing was defined in an independent cohort (N = 2001, aged 18-90 years). Neuroimaging data from HIV-positive and HIV-negative individuals were then used to estimate brain-predicted age; then brain-predicted age difference (brain-PAD = brain-predicted brain age - chronological age) scores were calculated. Neuropsychological and clinical assessments were also carried out.Results: HIV-positive individuals had greater brain-PAD score (mean ± SD = 2.15 ± 7.79 years) compared to HIV-negative individuals (-0.87 ± 8.40 years; b = 3.48, p < 0.01). Increased brain-PAD score was associated with decreased performance in multiple cognitive domains (information processing speed, executive function, memory) and general cognitive performance across all participants. Brain-PAD score was not associated with age, duration of HIV-infection or other HIV-related measures.Conclusions: Increased apparent brain ageing, predicted using neuroimaging, was observed in HIV-positive adults, despite effective viral suppression. Furthermore, the magnitude of increased apparent brain ageing related to cognitive deficits. However, predicted brain age difference did not correlate with chronological age or duration of HIV-infection, suggesting that HIV disease may accentuate, rather than accelerate brain ageing.

JOURNAL ARTICLE

Picchioni MM, Rijsdijk F, Toulopoulou T, Chaddock C, Cole JH, Ettinger U, Oses A, Metcalfe H, Murray RM, McGuire Pet 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

JOURNAL ARTICLE

Zhu D, Riedel BC, Jahanshad N, Groenewold NA, Stein DJ, Gotlib IH, Sacchet MD, Dima D, Cole JH, Fu CHY, Walter H, Veer IM, Frodl T, Schmaal L, Veltman DJ, Thompson PMet 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.

CONFERENCE PAPER

Jenkins PO, De Simoni S, Fleminger J, Bourke N, Jolly A, Cole J, Darian D, Sharp Det 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: 1468-330X

CONFERENCE PAPER

Feeney C, Scott GP, Cole JH, Sastre M, Goldstone AP, Leech Ret al., 2016, Seeds of neuroendocrine doubt, Nature, Vol: 535, Pages: E1-E2, ISSN: 0028-0836

JOURNAL ARTICLE

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