Comprehensive Assessment of Sleep Duration, Insomnia and Brain Structure within the UK Biobank Cohort

STUDY OBJECTIVES: To assess for associations between sleeping more than or less than recommended by the National Sleep Foundation (NSF), and self-reported insomnia, with brain structure. METHODS: Data from the UK Biobank cohort were analysed (N between 9K and 32K, dependent on availability, aged 44 to 82 years). Sleep measures included self-reported adherence to NSF guidelines on sleep duration (sleeping between 7 and 9 hours per night), and self-reported difficulty falling or staying asleep (insomnia). Brain structural measures included global and regional cortical or subcortical morphometry (thickness, surface area, volume), global and tract-related white matter microstructure, brain age gap (difference between chronological age and age estimated from brain scan), and total volume of white matter lesions. RESULTS: Longer-than-recommended sleep duration was associated with lower overall grey and white matter volumes, lower global and regional cortical thickness and volume measures, higher brain age gap, higher volume of white matter lesions, higher mean diffusivity globally and in thalamic and association fibers, and lower volume of the hippocampus. Shorter-than-recommended sleep duration was related to higher global and cerebellar white matter volumes, lower global and regional cortical surface areas, and lower fractional anisotropy in projection fibers. Self-reported insomnia was associated with higher global grey and white matter volumes, and with higher volumes of the amygdala, hippocampus and putamen. CONCLUSIONS: Sleeping longer than recommended by the NSF is associated with a wide range of differences in brain structure, potentially indicative of poorer brain health. Sleeping less than recommended is distinctly associated with lower cortical surface areas. Future studies should assess the potential mechanisms of these differences and investigate long sleep duration as a putative marker of brain health

Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes

There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population-wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size

Automated Classification of Depression from Structural Brain Measures across Two Independent Community-based Cohorts

ACKNOWLEDGEMENTS: This study was supported and funded by the Wellcome Trust Strategic Award ‘Stratifying Resilience and Depression Longitudinally’ (STRADL) (Reference 104036/Z/14/Z), and the Medical Research Council Mental Health Pathfinder Award ‘Leveraging routinely collected and linked research data to study the causes and consequences of common mental disorders’ (Reference MRC-MC_PC_17209). MAH is supported by research funding from the Dr Mortimer and Theresa Sackler Foundation. The research was conducted using the UK Biobank resource, with application number 4844. Structural brain imaging data from the UK Biobank was processed at the University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology (CCACE) http://www.ccace.ed.ac.uk/), which is a part of the crosscouncil Lifelong Health and Wellbeing Initiative (MR/K026992/1). CCACE received funding from Biotechnology and Biological Sciences Research Council (BBSRC), Medical Research Council (MRC), and was also supported by Age UK as part of The Disconnected Mind project. This work has made use of the resources provided by the Edinburgh Compute and Data Facility (ECDF) (http://www.ecdf.ed.ac.uk/)Peer reviewedPublisher PD

Epigenome-wide association study of global cortical volumes in Generation Scotland:Scottish Family Health Study

Funding This work was supported by the Wellcome Trust [104036/Z/14/Z]. Acknowledgements Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6] and the Scottish Funding Council [HR03006] and is currently supported by the Wellcome Trust [216767/Z/19/Z]. Genotyping of the GS:SFHS samples was carried out by the Genetics Core Laboratory at the Edinburgh Clinical Research Facility, University of Edinburgh, Scotland and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award “STratifying Resilience and Depression Longitudinally” (STRADL) Reference 104036/Z/14/Z). MCB is supported by a Guarantors of Brain Non-clinical Post-Doctoral Fellowship. AMM is supported by the Wellcome Trust (104036/Z/14/Z, 216767/Z/19/Z, 220857/Z/20/Z) and UKRI MRC (MC_PC_17209, MR/S035818/1). KLE is supported by the NARSAD Independent Investigator Award (Grant ID: 21956). JMW is supported by UK Dementia Research Institute which is funded by the MRC, Alzheimer’s Research UK and Alzheimer’s Society, by the Fondation Leducq (16 CVD 05), and the Row Fogo Centre for Research Into Ageing and the Brain (BRO- D.FID3668413). This work is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 847776.Peer reviewedPublisher PD

Disrupted limbic-prefrontal effective connectivity in response to fearful faces in lifetime depression

Background: Multiple brain imaging studies of negative emotional bias in major depressive disorder (MDD) have used images of fearful facial expressions and focused on the amygdala and the prefrontal cortex. The results have, however, been inconsistent, potentially due to small sample sizes (typically N < 50 ). It remains unclear if any alterations are a characteristic of current depression or of past experience of depression, and whether there are MDD-related changes in effective connectivity between the two brain regions.Methods: Activations and effective connectivity between the amygdala and dorsolateral prefrontal cortex (DLPFC) in response to fearful face stimuli were studied in a large population-based sample from Generation Scotland. Participants either had no history of MDD ( N = 664 in activation analyses, N = 474 in connectivity analyses) or had a diagnosis of MDD during their lifetime (LMDD, N = 290 in activation analyses, N = 214 in connectivity analyses). The within-scanner task involved implicit facial emotion processing of neutral and fearful faces.Results: Compared to controls, LMDD was associated with increased activations in left amygdala ( PFWE = 0.031 , k E = 4 ) and left DLPFC ( PFWE = 0.002 , k E = 33 ), increased mean bilateral amygdala activation ( β = 0.0715, P = 0.0314 ), and increased inhibition from left amygdala to left DLPFC, all in response to fearful faces contrasted to baseline. Results did not appear to be attributable to depressive illness severity or antidepressant medication status at scan time.Limitations: Most studied participants had past rather than current depression, average severity of ongoing depression symptoms was low, and a substantial proportion of participants were receiving medication. The study was not longitudinal and the participants were only assessed a single time.Conclusions: LMDD is associated with hyperactivity of the amygdala and DLPFC, and with stronger amygdala to DLPFC inhibitory connectivity, all in response to fearful faces, unrelated to depression severity at scan time. These results help reduce inconsistency in past literature and suggest disruption of ‘bottom-up’ limbic-prefrontal effective connectivity in depression

Identification of plasma proteins relating to brain neurodegeneration and vascular pathology in cognitively normal individuals

This project was funded by DPUK through MRC (grant no. MR/L023784/2) and the UK Medical Research Council Award to the University of Oxford (grant no. MC_PC_17215). L.S is funded by the Virtual Brain Cloud from European comission (grant no. H2020-SC1-DTH-2018-1). C.R.B is funded by National Institutes of Health (NIH) research grant R01AG054628. S.R.C is funded by National Institutes of Health (NIH) research grant (R01AG054628), Medical Research Council (MR/R024065/1), Age UK and Economic and Social Research Council. R.E.M. was supported by Alzheimer's Research UK major project grant ARUKPG2017B-10. C.H was supported by an MRC Human Genetics Unit programme grant “Quantitative traits in health and disease” (U.MC_UU_00007/10). H.C.W received funding from Wellcome Trust. J.W is funded by TauRx pharmaceuticals Ltd and received Educational grant from Biogen paid to Alzheimer Scotland/Brain Health Scotland. G.W received GRAMPIAN UNIVERSITY HOSPITALS NHS TRUST, Scottish Government—Chief Scientist Office, ROLAND SUTTON ACADEMIC TRUST, Medical Research Scotland, Sutton Academic Trust and ROLAND SUTTON ACADEMIC TRUST. J.M.W received Wellcome Trust Strategic Award, MRC UK Dementia Research Institute and MRC project grants, Fondation Leducq, Stroke Association, British Heart Foundation, Alzheimer Society, and the European Union H2020 PHC-03-15 SVDs@Target grant (666881). D.S received MRC (MR/S010351/1), MRC (MR/W002388/1) and MRC (MR/W002566/1). A.M is supported by the Wellcome Trust (104036/Z/14/Z, 216767/Z/19/Z, 220857/Z/20/Z) and UKRI MRC (MC_PC_17209, MR/S035818/1). This work is part of a project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 847776. In addition, A.M has received grant support from The Sackler Trust, outside of the work presented. N.B received grant to institution from GSK as part of GSK/Oxford FxG initiative. A.N.H received John Black Charitable Fund-Rosetrees, H2020 funding from European Comission-Project Virtual Brain Cloud, AI for the Discovery of new therapies in Parkinson's (A2926), Rising Start Initiative—stage 2, Brain-Gut Microbiome (Call: PAR-18-296; Award ID: 1U19AG063744-01), Gut-liver-brain biochemical axis in Alzheimer's disease (5RF1AG057452-01), Virtual Brain Cloud (Call: H2020-SC1-DTH- 2018-1; Grant agreement ID: 826421). Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006) and is currently supported by the Wellcome Trust (216767/Z/19/Z). Genotyping of the GS:SFHS samples was carried out by the Genetics Core Laboratory at the Edinburgh Clinical Research Facility, University of Edinburgh, Scotland and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award “STratifying Resilience and Depression Longitudinally” [STRADL] Reference 104036/Z/14/Z). We are grateful to all the families who took part; the general practitioners and the Scottish School of Primary Care for their help in recruiting them; and the whole Generation Scotland team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, health-care assistants, and nurses.Peer reviewedPublisher PD

Hair glucocorticoids are associated with childhood adversity, depressive symptoms and reduced global and lobar grey matter in Generation Scotland

ACKNOWLEDGEMENTS We would like to thank all of the Generation Scotland participants for their contribution to this study. We also thank the research assistants, clinicians and technicians for their help in collecting the data. Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6] and the Scottish Funding Council [HR03006] and is currently supported by the Wellcome Trust [216767/Z/19/Z]. This study was also supported and funded by the Wellcome Trust Strategic Award ‘Stratifying Resilience and Depression Longitudinally’ (STRADL) (Reference 104036/Z/14/Z). We acknowledge the support of the British Heart Foundation (RE/18/5/34216). CG is supported by the Medical Research Council and the University of Edinburgh through the Precision Medicine Doctoral Training Programme. MCB is supported by a Guarantors of Brain Non-Clinical Post-Doctoral Fellowship. JMW is funded by the UK Dementia Research Institute which is funded by the UK Medical Research Council, Alzheimer’s Research UK and Alzheimer’s SocietyPeer reviewedPublisher PD

Classification accuracy of structural and functional connectomes across different depressive phenotypes

Phenotyping of major depressive disorder (MDD) in research can vary from study to study, which, together with heterogeneity of the disorder, may contribute to the inconsistent associations with various risk factors including neuroimaging features. These aspects also potentially underlie previous problems with machine learning methods using imaging data to inform predictive biomarkers. In this study we therefore aimed to examine the classification accuracy of structural and functional connectomes across different depressive phenotypes, including separating MDD subgroups into those with and without early childhood adversity (one of the largest risk factors for MDD associated with brain development). We applied logistic ridge regression to classify control and MDD participants defined according to six different MDD definitions in a large community-based sample (N = 14, 507). We used brain connectomic data based on six structural and two functional network weightings and conducted a comprehensive analysis to (i) explore how well different connectome modalities predict different MDD phenotypes commonly used in research, (ii) investigate whether stratification of MDD based on the presence or absence of early childhood adversity (measured with the childhood trauma questionnaire) can improve prediction accuracies, and (iii) identify important predictive features that are consistent across MDD phenotypes. We find that functional connectomes consistently outperform structural connectomes as features for MDD classification across phenotypes. Highest accuracy of 61.06% (chance level 50.0%) was achieved when predicting the Currently Depressed phenotype (i.e. the phenotype defined by the presence of more than five symptoms of depression in the past two weeks) with features based on partial correlation functional connectomes. Accuracy of classifying Currently Depressed participants with added CTQ threshold criterion rose to 65.74%. Application of the Jaccard index to assess predictive feature overlap indicated that there were neurobiological differences between MDD patients with and without childhood adversity. Further to that, analysis of predictive features for different MDD phenotypes with binomial tests revealed sensorimotor and visual functional subnetworks as consistently important for prediction. Our results provide the basis for future research, and indicate that differences in sensorimotor and visual subnetworks may serve as important biomarkers of MDD