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Raman B, McCracken C, Cassar MP, Moss AJ, Finnigan L, Samat AHA, Ogbole G, Tunnicliffe EM, Alfaro-Almagro F, Menke R, Xie C, Gleeson F, Lukaschuk E, Lamlum H, McGlynn K, Popescu IA, Sanders ZB, Saunders LC, Piechnik SK, Ferreira VM, Nikolaidou C, Rahman NM, Ho LP, Harris VC, Shikotra A, Singapuri A, Pfeffer P, Manisty C, Kon OM, Beggs M, O'Regan DP, Fuld J, Weir-McCall JR, Parekh D, Steeds R, Poinasamy K, Cuthbertson DJ, Kemp GJ, Semple MG, Horsley A, Miller CA, O'Brien C, Shah AM, Chiribiri A, Leavy OC, Richardson M, Elneima O, McAuley HJC, Sereno M, Saunders RM, Houchen-Wolloff L, Greening NJ, Bolton CE, Brown JS, Choudhury G, Diar Bakerly N, Easom N, Echevarria C, Marks M, Hurst JR, Jones MG, Wootton DG, Chalder T, Davies MJ, De Soyza A, Geddes JR, Greenhalf W, Howard LS, Jacob J, Man WDC, Openshaw PJM, Porter JC, Rowland MJ, Scott JT, Singh SJ, Thomas DC, Toshner M, Lewis KE, Heaney LG, Harrison EM, Kerr S, Docherty AB, Lone NI, Quint J, Sheikh A, Zheng B, Jenkins RG, Cox E, Francis S, Halling-Brown M, Chalmers JD, Greenwood JP, Plein S, Hughes PJC, Thompson AAR, Rowland-Jones SL, Wild JM, Kelly M, Treibel TA, Bandula S, Aul R, Miller K, Jezzard P, Smith S, Nichols TE, McCann GP, Evans RA, Wain LV, Brightling CE, Neubauer S, Baillie JK, Shaw A, Hairsine B, Kurasz C, Henson H, Armstrong L, Shenton L, Dobson H, Dell A, Lucey A, Price A, Storrie A, Pennington C, Price C, Mallison G, Willis G, Nassa H, Haworth J, Hoare M, Hawkings N, Fairbairn S, Young S, Walker S, Jarrold I, Sanderson A, David C, Chong-James K, Zongo O, James WY, Martineau A, King B, Armour C, McAulay D, Major E, McGinness J, McGarvey L, Magee N, Stone R, Drain S, Craig T, Bolger A, Haggar A, Lloyd A, Subbe C, Menzies D, Southern D, McIvor E, Roberts K, Manley R, Whitehead V, Saxon W, Bularga A, Mills NL, El-Taweel H, Dawson J, Robinson L, Saralaya D, Regan K, Storton K, Brear L, Amoils S, Bermperi A, Elmer A, Ribeiro C, Cruz I, Taylor J, Worsley J, Dempsey K, Watson L, Jose S, Marciniak S, Parkes M, McQueen A, Oliver C, Williams J, Paradowski K, Broad L, Knibbs L, Haynes M, Sabit R, Milligan L, Sampson C, Hancock A, Evenden C, Lynch C, Hancock K, Roche L, Rees M, Stroud N, Thomas-Woods T, Heller S, Robertson E, Young B, Wassall H, Babores M, Holland M, Keenan N, Shashaa S, Price C, Beranova E, Ramos H, Weston H, Deery J, Austin L, Solly R, Turney S, Cosier T, Hazelton T, Ralser M, Wilson A, Pearce L, Pugmire S, Stoker W, McCormick W, Dewar A, Arbane G, Kaltsakas G, Kerslake H, Rossdale J, Bisnauthsing K, Aguilar Jimenez LA, Martinez LM, Ostermann M, Magtoto MM, Hart N, Marino P, Betts S, Solano TS, Arias AM, Prabhu A, Reed A, Wrey Brown C, Griffin D, Bevan E, Martin J, Owen J, Alvarez Corral M, Williams N, Payne S, Storrar W, Layton A, Lawson C, Mills C, Featherstone J, Stephenson L, Burdett T, Ellis Y, Richards A, Wright C, Sykes DL, Brindle K, Drury K, Holdsworth L, Crooks MG, Atkin P, Flockton R, Thackray-Nocera S, Mohamed A, Taylor A, Perkins E, Ross G, McGuinness H, Tench H, Phipps J, Loosley R, Wolf-Roberts R, Coetzee S, Omar Z, Ross A, Card B, Carr C, King C, Wood C, Copeland D, Calvelo E, Chilvers ER, Russell E, Gordon H, Nunag JL, Schronce J, March K, Samuel K, Burden L, Evison L, McLeavey L, Orriss-Dib L, Tarusan L, Mariveles M, Roy M, Mohamed N, Simpson N, Yasmin N, Cullinan P, Daly P, Haq S, Moriera S, Fayzan T, Munawar U, Nwanguma U, Lingford-Hughes A, Altmann D, Johnston D, Mitchell J, Valabhji J, Price L, Molyneaux PL, Thwaites RS, Walsh S, Frankel A, Lightstone L, Wilkins M, Willicombe M, McAdoo S, Touyz R, Guerdette AM, Warwick K, Hewitt M, Reddy R, White S, McMahon A, Hoare A, Knighton A, Ramos A, Te A, Jolley CJ, Speranza F, Assefa-Kebede H, Peralta I, Breeze J, Shevket K, Powell N, Adeyemi O, Dulawan P, Adrego R, Byrne S, Patale S, Hayday A, Malim M, Pariante C, Sharpe C, Whitney J, Bramham K, Ismail K, Wessely S, Nicholson T, Ashworth A, Humphries A, Tan AL, Whittam B, Coupland C, Favager C, Peckham D, Wade E, Saalmink G, Clarke J, Glossop J, Murira J, Rangeley J, Woods J, Hall L, Dalton M, Window N, Beirne P, Hardy T, Coakley G, Turtle L, Berridge A, Cross A, Key AL, Rowe A, Allt AM, Mears C, Malein F, Madzamba G, Hardwick HE, Earley J, Hawkes J, Pratt J, Wyles J, Tripp KA, Hainey K, Allerton L, Lavelle-Langham L, Melling L, Wajero LO, Poll L, Noonan MJ, French N, Lewis-Burke N, Williams-Howard SA, Cooper S, Kaprowska S, Dobson SL, Marsh S, Highett V, Shaw V, Beadsworth M, Defres S, Watson E, Tiongson GF, Papineni P, Gurram S, Diwanji SN, Quaid S, Briggs A, Hastie C, Rogers N, Stensel D, Bishop L, McIvor K, Rivera-Ortega P, Al-Sheklly B, Avram C, Faluyi D, Blaikely J, Piper Hanley K, Radhakrishnan K, Buch M, Hanley NA, Odell N, Osbourne R, Stockdale S, Felton T, Gorsuch T, Hussell T, Kausar Z, Kabir T, McAllister-Williams H, Paddick S, Burn D, Ayoub A, Greenhalgh A, Sayer A, Young A, Price D, Burns G, MacGowan G, Fisher H, Tedd H, Simpson J, Jiwa K, Witham M, Hogarth P, West S, Wright S, McMahon MJ, Neill P, Dougherty A, Morrow A, Anderson D, Grieve D, Bayes H, Fallon K, Mangion K, Gilmour L, Basu N, Sykes R, Berry C, McInnes IB, Donaldson A, Sage EK, Barrett F, Welsh B, Bell M, Quigley J, Leitch K, Macliver L, Patel M, Hamil R, Deans A, Furniss J, Clohisey S, Elliott A, Solstice AR, Deas C, Tee C, Connell D, Sutherland D, George J, Mohammed S, Bunker J, Holmes K, Dipper A, Morley A, Arnold D, Adamali H, Welch H, Morrison L, Stadon L, Maskell N, Barratt S, Dunn S, Waterson S, Jayaraman B, Light T, Selby N, Hosseini A, Shaw K, Almeida P, Needham R, Thomas AK, Matthews L, Gupta A, Nikolaidis A, Dupont C, Bonnington J, Chrystal M, Greenhaff PL, Linford S, Prosper S, Jang W, Alamoudi A, Bloss A, Megson C, Nicoll D, Fraser E, Pacpaco E, Conneh F, Ogg G, McShane H, Koychev I, Chen J, Pimm J, Ainsworth M, Pavlides M, Sharpe M, Havinden-Williams M, Petousi N, Talbot N, Carter P, Kurupati P, Dong T, Peng Y, Burns A, Kanellakis N, Korszun A, Connolly B, Busby J, Peto T, Patel B, Nolan CM, Cristiano D, Walsh JA, Liyanage K, Gummadi M, Dormand N, Polgar O, George P, Barker RE, Patel S, Price L, Gibbons M, Matila D, Jarvis H, Lim L, Olaosebikan O, Ahmad S, Brill S, Mandal S, Laing C, Michael A, Reddy A, Johnson C, Baxendale H, Parfrey H, Mackie J, Newman J, Pack J, Parmar J, Paques K, Garner L, Harvey A, Summersgill C, Holgate D, Hardy E, Oxton J, Pendlebury J, McMorrow L, Mairs N, Majeed N, Dark P, Ugwuoke R, Knight S, Whittaker S, Strong-Sheldrake S, Matimba-Mupaya W, Chowienczyk P, Pattenadk D, Hurditch E, Chan F, Carborn H, Foot H, Bagshaw J, Hockridge J, Sidebottom J, Lee JH, Birchall K, Turner K, Haslam L, Holt L, Milner L, Begum M, Marshall M, Steele N, Tinker N, Ravencroft P, Butcher R, Misra S, Walker S, Coburn Z, Fairman A, Ford A, Holbourn A, Howell A, Lawrie A, Lye A, Mbuyisa A, Zawia A, Holroyd-Hind B, Thamu B, Clark C, Jarman C, Norman C, Roddis C, Foote D, Lee E, Ilyas F, Stephens G, Newell H, Turton H, Macharia I, Wilson I, Cole J, McNeill J, Meiring J, Rodger J, Watson J, Chapman K, Harrington K, Chetham L, Hesselden L, Nwafor L, Dixon M, Plowright M, Wade P, Gregory R, Lenagh R, Stimpson R, Megson S, Newman T, Cheng Y, Goodwin C, Heeley C, Sissons D, Sowter D, Gregory H, Wynter I, Hutchinson J, Kirk J, Bennett K, Slack K, Allsop L, Holloway L, Flynn M, Gill M, Greatorex M, Holmes M, Buckley P, Shelton S, Turner S, Sewell TA, Whitworth V, Lovegrove W, Tomlinson J, Warburton L, Painter S, Vickers C, Redwood D, Tilley J, Palmer S, Wainwright T, Breen G, Hotopf M, Dunleavy A, Teixeira J, Ali M, Mencias M, Msimanga N, Siddique S, Samakomva T, Tavoukjian V, Forton D, Ahmed R, Cook A, Thaivalappil F, Connor L, Rees T, McNarry M, Williams N, McCormick J, McIntosh J, Vere J, Coulding M, Kilroy S, Turner V, Butt AT, Savill H, Fraile E, Ugoji J, Landers G, Lota H, Portukhay S, Nasseri M, Daniels A, Hormis A, Ingham J, Zeidan L, Osborne L, Chablani M, Banerjee A, David A, Pakzad A, Rangelov B, Williams B, Denneny E, Willoughby J, Xu M, Mehta P, Batterham R, Bell R, Aslani S, Lilaonitkul W, Checkley A, Bang D, Basire D, Lomas D, Wall E, Plant H, Roy K, Heightman M, Lipman M, Merida Morillas M, Ahwireng N, Chambers RC, Jastrub R, Logan S, Hillman T, Botkai A, Casey A, Neal A, Newton-Cox A, Cooper B, Atkin C, McGee C, Welch C, Wilson D, Sapey E, Qureshi H, Hazeldine J, Lord JM, Nyaboko J, Short J, Stockley J, Dasgin J, Draxlbauer K, Isaacs K, Mcgee K, Yip KP, Ratcliffe L, Bates M, Ventura M, Ahmad Haider N, Gautam N, Baggott R, Holden S, Madathil S, Walder S, Yasmin S, Hiwot T, Jackson T, Soulsby T, Kamwa V, Peterkin Z, Suleiman Z, Chaudhuri N, Wheeler H, Djukanovic R, Samuel R, Sass T, Wallis T, Marshall B, Childs C, Marouzet E, Harvey M, Fletcher S, Dickens C, Beckett P, Nanda U, Daynes E, Charalambou A, Yousuf AJ, Lea A, Prickett A, Gooptu B, Hargadon B, Bourne C, Christie C, Edwardson C, Lee D, Baldry E, Stringer E, Woodhead F, Mills G, Arnold H, Aung H, Qureshi IN, Finch J, Skeemer J, Hadley K, Khunti K, Carr L, Ingram L, Aljaroof M, Bakali M, Bakau M, Baldwin M, Bourne M, Pareek M, Soares M, Tobin M, Armstrong N, Brunskill N, Goodman N, Cairns P, Haldar P, McCourt P, Dowling R, Russell R, Diver S, Edwards S, Glover S, Parker S, Siddiqui S, Ward TJC, Mcnally T, Thornton T, Yates T, Ibrahim W, Monteiro W, Thickett D, Wilkinson D, Broome M, McArdle P, Upthegrove R, Wraith D, Langenberg C, Summers C, Bullmore E, Heeney JL, Schwaeble W, Sudlow CL, Adeloye D, Newby DE, Rudan I, Shankar-Hari M, Thorpe M, Pius R, Walmsley S, McGovern A, Ballard C, Allan L, Dennis J, Cavanagh J, Petrie J, O'Donnell K, Spears M, Sattar N, MacDonald S, Guthrie E, Henderson M, Guillen Guio B, Zhao B, Lawson C, Overton C, Taylor C, Tong C, Mukaetova-Ladinska E, Turner E, Pearl JE, Sargant J, Wormleighton J, Bingham M, Sharma M, Steiner M, Samani N, Novotny P, Free R, Allen RJ, Finney S, Terry S, Brugha T, Plekhanova T, McArdle A, Vinson B, Spencer LG, Reynolds W, Ashworth M, Deakin B, Chinoy H, Abel K, Harvie M, Stanel S, Rostron A, Coleman C, Baguley D, Hufton E, Khan F, Hall I, Stewart I, Fabbri L, Wright L, Kitterick P, Morriss R, Johnson S, Bates A, Antoniades C, Clark D, Bhui K, Channon KM, Motohashi K, Sigfrid L, Husain M, Webster M, Fu X, Li X, Kingham L, Klenerman P, Miiler K, Carson G, Simons G, Huneke N, Calder PC, Baldwin D, Bain S, Lasserson D, Daines L, Bright E, Stern M, Crisp P, Dharmagunawardena R, Reddington A, Wight A, Bailey L, Ashish A, Robinson E, Cooper J, Broadley A, Turnbull A, Brookes C, Sarginson C, Ionita D, Redfearn H, Elliott K, Barman L, Griffiths L, Guy Z, Gill R, Nathu R, Harris E, Moss P, Finnigan J, Saunders K, Saunders P, Kon S, Kon SS, O'Brien L, Shah K, Shah P, Richardson E, Brown V, Brown M, Brown J, Brown J, Brown A, Brown A, Brown M, Choudhury N, Jones S, Jones H, Jones L, Jones I, Jones G, Jones H, Jones D, Davies F, Davies E, Davies K, Davies G, Davies GA, Howard K, Porter J, Rowland J, Rowland A, Scott K, Singh S, Singh C, Thomas S, Thomas C, Lewis V, Lewis J, Lewis D, Harrison P, Francis C, Francis R, Hughes RA, Hughes J, Hughes AD, Thompson T, Kelly S, Smith D, Smith N, Smith A, Smith J, Smith L, Smith S, Evans T, Evans RI, Evans D, Evans R, Evans H, Evans J. Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study. Lancet Respir Med 2023; 11:1003-1019. [PMID: 37748493 PMCID: PMC7615263 DOI: 10.1016/s2213-2600(23)00262-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 06/16/2023] [Accepted: 06/30/2023] [Indexed: 09/27/2023]
Abstract
INTRODUCTION The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures. METHODS In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov, NCT04510025. FINDINGS Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2-6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MRI were more frequent in patients than in controls (157 [61%] of 259 vs 14 [27%] of 52; p<0·0001) and independently associated with COVID-19 status (odds ratio [OR] 2·9 [95% CI 1·5-5·8]; padjusted=0·0023) after adjusting for relevant confounders. Compared with controls, patients were more likely to have MRI evidence of lung abnormalities (p=0·0001; parenchymal abnormalities), brain abnormalities (p<0·0001; more white matter hyperintensities and regional brain volume reduction), and kidney abnormalities (p=0·014; lower medullary T1 and loss of corticomedullary differentiation), whereas cardiac and liver MRI abnormalities were similar between patients and controls. Patients with multiorgan abnormalities were older (difference in mean age 7 years [95% CI 4-10]; mean age of 59·8 years [SD 11·7] with multiorgan abnormalities vs mean age of 52·8 years [11·9] without multiorgan abnormalities; p<0·0001), more likely to have three or more comorbidities (OR 2·47 [1·32-4·82]; padjusted=0·0059), and more likely to have a more severe acute infection (acute CRP >5mg/L, OR 3·55 [1·23-11·88]; padjusted=0·025) than those without multiorgan abnormalities. Presence of lung MRI abnormalities was associated with a two-fold higher risk of chest tightness, and multiorgan MRI abnormalities were associated with severe and very severe persistent physical and mental health impairment (PHOSP-COVID symptom clusters) after hospitalisation. INTERPRETATION After hospitalisation for COVID-19, people are at risk of multiorgan abnormalities in the medium term. Our findings emphasise the need for proactive multidisciplinary care pathways, with the potential for imaging to guide surveillance frequency and therapeutic stratification. FUNDING UK Research and Innovation and National Institute for Health Research.
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Sundaresan V, Arthofer C, Zamboni G, Murchison AG, Dineen RA, Rothwell PM, Auer DP, Wang C, Miller KL, Tendler BC, Alfaro-Almagro F, Sotiropoulos SN, Sprigg N, Griffanti L, Jenkinson M. Automated detection of cerebral microbleeds on MR images using knowledge distillation framework. Front Neuroinform 2023; 17:1204186. [PMID: 37492242 PMCID: PMC10363739 DOI: 10.3389/fninf.2023.1204186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 06/19/2023] [Indexed: 07/27/2023] Open
Abstract
Introduction Cerebral microbleeds (CMBs) are associated with white matter damage, and various neurodegenerative and cerebrovascular diseases. CMBs occur as small, circular hypointense lesions on T2*-weighted gradient recalled echo (GRE) and susceptibility-weighted imaging (SWI) images, and hyperintense on quantitative susceptibility mapping (QSM) images due to their paramagnetic nature. Accurate automated detection of CMBs would help to determine quantitative imaging biomarkers (e.g., CMB count) on large datasets. In this work, we propose a fully automated, deep learning-based, 3-step algorithm, using structural and anatomical properties of CMBs from any single input image modality (e.g., GRE/SWI/QSM) for their accurate detections. Methods In our method, the first step consists of an initial candidate detection step that detects CMBs with high sensitivity. In the second step, candidate discrimination step is performed using a knowledge distillation framework, with a multi-tasking teacher network that guides the student network to classify CMB and non-CMB instances in an offline manner. Finally, a morphological clean-up step further reduces false positives using anatomical constraints. We used four datasets consisting of different modalities specified above, acquired using various protocols and with a variety of pathological and demographic characteristics. Results On cross-validation within datasets, our method achieved a cluster-wise true positive rate (TPR) of over 90% with an average of <2 false positives per subject. The knowledge distillation framework improves the cluster-wise TPR of the student model by 15%. Our method is flexible in terms of the input modality and provides comparable cluster-wise TPR and better cluster-wise precision compared to existing state-of-the-art methods. When evaluating across different datasets, our method showed good generalizability with a cluster-wise TPR >80 % with different modalities. The python implementation of the proposed method is openly available.
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Affiliation(s)
- Vaanathi Sundaresan
- Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, Karnataka, India
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Christoph Arthofer
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- National Institute for Health and Care Research (NIHR) Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
| | - Giovanna Zamboni
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Dipartimento di Scienze Biomediche, Metaboliche e Neuroscienze, Universitá di Modena e Reggio Emilia, Modena, Italy
| | - Andrew G. Murchison
- Department of Neuroradiology, Oxford University Hospitals National Health Service (NHS) Foundation Trust, Oxford, United Kingdom
| | - Robert A. Dineen
- National Institute for Health and Care Research (NIHR) Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
- Radiological Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Peter M. Rothwell
- Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Dorothee P. Auer
- National Institute for Health and Care Research (NIHR) Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
- Radiological Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Chaoyue Wang
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Benjamin C. Tendler
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Stamatios N. Sotiropoulos
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- National Institute for Health and Care Research (NIHR) Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
| | - Nikola Sprigg
- Stroke Trials Unit, Mental Health and Clinical Neuroscience, University of Nottingham, Nottingham, United Kingdom
| | - Ludovica Griffanti
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- South Australian Health and Medical Research Institute, Adelaide, SA, Australia
- Australian Institute for Machine Learning, School of Computer Science, The University of Adelaide, Adelaide, SA, Australia
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Topiwala A, Mankia K, Bell S, Webb A, Ebmeier KP, Howard I, Wang C, Alfaro-Almagro F, Miller K, Burgess S, Smith S, Nichols TE. Association of gout with brain reserve and vulnerability to neurodegenerative disease. Nat Commun 2023; 14:2844. [PMID: 37202397 PMCID: PMC10195870 DOI: 10.1038/s41467-023-38602-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 05/09/2023] [Indexed: 05/20/2023] Open
Abstract
Studies of neurodegenerative disease risk in gout are contradictory. Relationships with neuroimaging markers of brain structure, which may offer insights, are uncertain. Here we investigated associations between gout, brain structure, and neurodegenerative disease incidence. Gout patients had smaller global and regional brain volumes and markers of higher brain iron, using both observational and genetic approaches. Participants with gout also had higher incidence of all-cause dementia, Parkinson's disease, and probable essential tremor. Risks were strongly time dependent, whereby associations with incident dementia were highest in the first 3 years after gout diagnosis. These findings suggest gout is causally related to several measures of brain structure. Lower brain reserve amongst gout patients may explain their higher vulnerability to multiple neurodegenerative diseases. Motor and cognitive impairments may affect gout patients, particularly in early years after diagnosis.
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Affiliation(s)
- Anya Topiwala
- Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK.
| | - Kulveer Mankia
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds and NIHR Leeds Biomedical Research Centre, Leeds Teaching Hospitals NHS Trust, Chapel Allerton Hospital, Leeds, UK
| | - Steven Bell
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Alastair Webb
- Wolfson Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Klaus P Ebmeier
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Isobel Howard
- Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK
| | - Chaoyue Wang
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Shanghai, China
| | - Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Karla Miller
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Stephen Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Thomas E Nichols
- Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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Bass C, Silva MD, Sudre C, Williams LZJ, Sousa HS, Tudosiu PD, Alfaro-Almagro F, Fitzgibbon SP, Glasser MF, Smith SM, Robinson EC. ICAM-Reg: Interpretable Classification and Regression With Feature Attribution for Mapping Neurological Phenotypes in Individual Scans. IEEE Trans Med Imaging 2023; 42:959-970. [PMID: 36374873 DOI: 10.1109/tmi.2022.3221890] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
An important goal of medical imaging is to be able to precisely detect patterns of disease specific to individual scans; however, this is challenged in brain imaging by the degree of heterogeneity of shape and appearance. Traditional methods, based on image registration, historically fail to detect variable features of disease, as they utilise population-based analyses, suited primarily to studying group-average effects. In this paper we therefore take advantage of recent developments in generative deep learning to develop a method for simultaneous classification, or regression, and feature attribution (FA). Specifically, we explore the use of a VAE-GAN (variational autoencoder - general adversarial network) for translation called ICAM, to explicitly disentangle class relevant features, from background confounds, for improved interpretability and regression of neurological phenotypes. We validate our method on the tasks of Mini-Mental State Examination (MMSE) cognitive test score prediction for the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, as well as brain age prediction, for both neurodevelopment and neurodegeneration, using the developing Human Connectome Project (dHCP) and UK Biobank datasets. We show that the generated FA maps can be used to explain outlier predictions and demonstrate that the inclusion of a regression module improves the disentanglement of the latent space. Our code is freely available on GitHub https://github.com/CherBass/ICAM.
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5
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Topiwala A, Nichols TE, Williams LZJ, Robinson EC, Alfaro-Almagro F, Taschler B, Wang C, Nelson CP, Miller KL, Codd V, Samani NJ, Smith SM. Telomere length and brain imaging phenotypes in UK Biobank. PLoS One 2023; 18:e0282363. [PMID: 36947528 PMCID: PMC10032499 DOI: 10.1371/journal.pone.0282363] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 02/13/2023] [Indexed: 03/23/2023] Open
Abstract
Telomeres form protective caps at the ends of chromosomes, and their attrition is a marker of biological aging. Short telomeres are associated with an increased risk of neurological and psychiatric disorders including dementia. The mechanism underlying this risk is unclear, and may involve brain structure and function. However, the relationship between telomere length and neuroimaging markers is poorly characterized. Here we show that leucocyte telomere length (LTL) is associated with multi-modal MRI phenotypes in 31,661 UK Biobank participants. Longer LTL is associated with: i) larger global and subcortical grey matter volumes including the hippocampus, ii) lower T1-weighted grey-white tissue contrast in sensory cortices, iii) white-matter microstructure measures in corpus callosum and association fibres, iv) lower volume of white matter hyperintensities, and v) lower basal ganglia iron. Longer LTL was protective against certain related clinical manifestations, namely all-cause dementia (HR 0.93, 95% CI: 0.91-0.96), but not stroke or Parkinson's disease. LTL is associated with multiple MRI endophenotypes of neurodegenerative disease, suggesting a pathway by which longer LTL may confer protective against dementia.
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Affiliation(s)
- Anya Topiwala
- Nuffield Department Population Health, Big Data Institute, University of Oxford, Oxford, United Kingdom
| | - Thomas E. Nichols
- Nuffield Department Population Health, Big Data Institute, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, Oxford, United Kingdom
| | - Logan Z. J. Williams
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Emma C. Robinson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Fidel Alfaro-Almagro
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Bernd Taschler
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Chaoyue Wang
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Oxford University, Oxford, United Kingdom
| | - Christopher P. Nelson
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Oxford University, Oxford, United Kingdom
| | - Veryan Codd
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Nilesh J. Samani
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Stephen M. Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Oxford University, Oxford, United Kingdom
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6
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Wang C, Martins-Bach AB, Alfaro-Almagro F, Douaud G, Klein JC, Llera A, Fiscone C, Bowtell R, Elliott LT, Smith SM, Tendler BC, Miller KL. Phenotypic and genetic associations of quantitative magnetic susceptibility in UK Biobank brain imaging. Nat Neurosci 2022; 25:818-831. [PMID: 35606419 PMCID: PMC9174052 DOI: 10.1038/s41593-022-01074-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 04/11/2022] [Indexed: 12/17/2022]
Abstract
A key aim in epidemiological neuroscience is identification of markers to assess brain health and monitor therapeutic interventions. Quantitative susceptibility mapping (QSM) is an emerging magnetic resonance imaging technique that measures tissue magnetic susceptibility and has been shown to detect pathological changes in tissue iron, myelin and calcification. We present an open resource of QSM-based imaging measures of multiple brain structures in 35,273 individuals from the UK Biobank prospective epidemiological study. We identify statistically significant associations of 251 phenotypes with magnetic susceptibility that include body iron, disease, diet and alcohol consumption. Genome-wide associations relate magnetic susceptibility to 76 replicating clusters of genetic variants with biological functions involving iron, calcium, myelin and extracellular matrix. These patterns of associations include relationships that are unique to QSM, in particular being complementary to T2* signal decay time measures. These new imaging phenotypes are being integrated into the core UK Biobank measures provided to researchers worldwide, creating the potential to discover new, non-invasive markers of brain health.
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Affiliation(s)
- Chaoyue Wang
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Aurea B Martins-Bach
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Gwenaëlle Douaud
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Johannes C Klein
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
| | - Alberto Llera
- Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, the Netherlands
| | - Cristiana Fiscone
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Richard Bowtell
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Lloyd T Elliott
- Department of Statistics and Actuarial Science, Simon Fraser University, Vancouver, British Columbia, Canada
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Benjamin C Tendler
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
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7
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Douaud G, Lee S, Alfaro-Almagro F, Arthofer C, Wang C, McCarthy P, Lange F, Andersson JLR, Griffanti L, Duff E, Jbabdi S, Taschler B, Keating P, Winkler AM, Collins R, Matthews PM, Allen N, Miller KL, Nichols TE, Smith SM. SARS-CoV-2 is associated with changes in brain structure in UK Biobank. Nature 2022. [DOI: 10.1038/s41586-022-04569-5 3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
AbstractThere is strong evidence of brain-related abnormalities in COVID-191–13. However, it remains unknown whether the impact of SARS-CoV-2 infection can be detected in milder cases, and whether this can reveal possible mechanisms contributing to brain pathology. Here we investigated brain changes in 785 participants of UK Biobank (aged 51–81 years) who were imaged twice using magnetic resonance imaging, including 401 cases who tested positive for infection with SARS-CoV-2 between their two scans—with 141 days on average separating their diagnosis and the second scan—as well as 384 controls. The availability of pre-infection imaging data reduces the likelihood of pre-existing risk factors being misinterpreted as disease effects. We identified significant longitudinal effects when comparing the two groups, including (1) a greater reduction in grey matter thickness and tissue contrast in the orbitofrontal cortex and parahippocampal gyrus; (2) greater changes in markers of tissue damage in regions that are functionally connected to the primary olfactory cortex; and (3) a greater reduction in global brain size in the SARS-CoV-2 cases. The participants who were infected with SARS-CoV-2 also showed on average a greater cognitive decline between the two time points. Importantly, these imaging and cognitive longitudinal effects were still observed after excluding the 15 patients who had been hospitalised. These mainly limbic brain imaging results may be the in vivo hallmarks of a degenerative spread of the disease through olfactory pathways, of neuroinflammatory events, or of the loss of sensory input due to anosmia. Whether this deleterious effect can be partially reversed, or whether these effects will persist in the long term, remains to be investigated with additional follow-up.
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8
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Douaud G, Lee S, Alfaro-Almagro F, Arthofer C, Wang C, McCarthy P, Lange F, Andersson JLR, Griffanti L, Duff E, Jbabdi S, Taschler B, Keating P, Winkler AM, Collins R, Matthews PM, Allen N, Miller KL, Nichols TE, Smith SM. SARS-CoV-2 is associated with changes in brain structure in UK Biobank. Nature 2022; 604:697-707. [PMID: 35255491 PMCID: PMC9046077 DOI: 10.1038/s41586-022-04569-5] [Citation(s) in RCA: 673] [Impact Index Per Article: 336.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 02/21/2022] [Indexed: 01/01/2023]
Abstract
There is strong evidence of brain-related abnormalities in COVID-191-13. However, it remains unknown whether the impact of SARS-CoV-2 infection can be detected in milder cases, and whether this can reveal possible mechanisms contributing to brain pathology. Here we investigated brain changes in 785 participants of UK Biobank (aged 51-81 years) who were imaged twice using magnetic resonance imaging, including 401 cases who tested positive for infection with SARS-CoV-2 between their two scans-with 141 days on average separating their diagnosis and the second scan-as well as 384 controls. The availability of pre-infection imaging data reduces the likelihood of pre-existing risk factors being misinterpreted as disease effects. We identified significant longitudinal effects when comparing the two groups, including (1) a greater reduction in grey matter thickness and tissue contrast in the orbitofrontal cortex and parahippocampal gyrus; (2) greater changes in markers of tissue damage in regions that are functionally connected to the primary olfactory cortex; and (3) a greater reduction in global brain size in the SARS-CoV-2 cases. The participants who were infected with SARS-CoV-2 also showed on average a greater cognitive decline between the two time points. Importantly, these imaging and cognitive longitudinal effects were still observed after excluding the 15 patients who had been hospitalised. These mainly limbic brain imaging results may be the in vivo hallmarks of a degenerative spread of the disease through olfactory pathways, of neuroinflammatory events, or of the loss of sensory input due to anosmia. Whether this deleterious effect can be partially reversed, or whether these effects will persist in the long term, remains to be investigated with additional follow-up.
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Affiliation(s)
- Gwenaëlle Douaud
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Soojin Lee
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Fidel Alfaro-Almagro
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Christoph Arthofer
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Chaoyue Wang
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Paul McCarthy
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Frederik Lange
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Jesper L R Andersson
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Ludovica Griffanti
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- OHBA, Wellcome Centre for Integrative Neuroimaging (WIN), Department of Psychiatry, University of Oxford, Oxford, UK
| | - Eugene Duff
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Department of Paediatrics, University of Oxford, Oxford, UK
| | - Saad Jbabdi
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Bernd Taschler
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Peter Keating
- Ear Institute, University College London, London, UK
| | - Anderson M Winkler
- National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Rory Collins
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Paul M Matthews
- UK Dementia Research Institute and Department of Brain Sciences, Imperial College, London, UK
| | - Naomi Allen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Karla L Miller
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | | | - Stephen M Smith
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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9
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Douaud G, Lee S, Alfaro-Almagro F, Arthofer C, Wang C, McCarthy P, Lange F, Andersson JLR, Griffanti L, Duff E, Jbabdi S, Taschler B, Keating P, Winkler AM, Collins R, Matthews PM, Allen N, Miller KL, Nichols TE, Smith SM. SARS-CoV-2 is associated with changes in brain structure in UK Biobank. medRxiv 2022:2021.06.11.21258690. [PMID: 34189535 PMCID: PMC8240690 DOI: 10.1101/2021.06.11.21258690] [Citation(s) in RCA: 60] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
There is strong evidence for brain-related abnormalities in COVID-19 1-13 . It remains unknown however whether the impact of SARS-CoV-2 infection can be detected in milder cases, and whether this can reveal possible mechanisms contributing to brain pathology. Here, we investigated brain changes in 785 UK Biobank participants (aged 51-81) imaged twice, including 401 cases who tested positive for infection with SARS-CoV-2 between their two scans, with 141 days on average separating their diagnosis and second scan, and 384 controls. The availability of pre-infection imaging data reduces the likelihood of pre-existing risk factors being misinterpreted as disease effects. We identified significant longitudinal effects when comparing the two groups, including: (i) greater reduction in grey matter thickness and tissue-contrast in the orbitofrontal cortex and parahippocampal gyrus, (ii) greater changes in markers of tissue damage in regions functionally-connected to the primary olfactory cortex, and (iii) greater reduction in global brain size. The infected participants also showed on average larger cognitive decline between the two timepoints. Importantly, these imaging and cognitive longitudinal effects were still seen after excluding the 15 cases who had been hospitalised. These mainly limbic brain imaging results may be the in vivo hallmarks of a degenerative spread of the disease via olfactory pathways, of neuroinflammatory events, or of the loss of sensory input due to anosmia. Whether this deleterious impact can be partially reversed, or whether these effects will persist in the long term, remains to be investigated with additional follow up.
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10
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Sundaresan V, Arthofer C, Zamboni G, Dineen RA, Rothwell PM, Sotiropoulos SN, Auer DP, Tozer DJ, Markus HS, Miller KL, Dragonu I, Sprigg N, Alfaro-Almagro F, Jenkinson M, Griffanti L. Automated Detection of Candidate Subjects With Cerebral Microbleeds Using Machine Learning. Front Neuroinform 2022; 15:777828. [PMID: 35126079 PMCID: PMC8811357 DOI: 10.3389/fninf.2021.777828] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 12/23/2021] [Indexed: 11/21/2022] Open
Abstract
Cerebral microbleeds (CMBs) appear as small, circular, well defined hypointense lesions of a few mm in size on T2*-weighted gradient recalled echo (T2*-GRE) images and appear enhanced on susceptibility weighted images (SWI). Due to their small size, contrast variations and other mimics (e.g., blood vessels), CMBs are highly challenging to detect automatically. In large datasets (e.g., the UK Biobank dataset), exhaustively labelling CMBs manually is difficult and time consuming. Hence it would be useful to preselect candidate CMB subjects in order to focus on those for manual labelling, which is essential for training and testing automated CMB detection tools on these datasets. In this work, we aim to detect CMB candidate subjects from a larger dataset, UK Biobank, using a machine learning-based, computationally light pipeline. For our evaluation, we used 3 different datasets, with different intensity characteristics, acquired with different scanners. They include the UK Biobank dataset and two clinical datasets with different pathological conditions. We developed and evaluated our pipelines on different types of images, consisting of SWI or GRE images. We also used the UK Biobank dataset to compare our approach with alternative CMB preselection methods using non-imaging factors and/or imaging data. Finally, we evaluated the pipeline's generalisability across datasets. Our method provided subject-level detection accuracy > 80% on all the datasets (within-dataset results), and showed good generalisability across datasets, providing a consistent accuracy of over 80%, even when evaluated across different modalities.
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Affiliation(s)
- Vaanathi Sundaresan
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
- Oxford-Nottingham Centre for Doctoral Training in Biomedical Imaging, University of Oxford, Oxford, United Kingdom
| | - Christoph Arthofer
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
- NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
| | - Giovanna Zamboni
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
- Wolfson Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Dipartimento di Scienze Biomediche, Metaboliche e Neuroscienze, Università di Modena e Reggio Emilia, Modena, Italy
| | - Robert A. Dineen
- NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
- Radiological Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Peter M. Rothwell
- Wolfson Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Stamatios N. Sotiropoulos
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
- NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
| | - Dorothee P. Auer
- NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
- Radiological Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Daniel J. Tozer
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Hugh S. Markus
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Karla L. Miller
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
| | - Iulius Dragonu
- Siemens Healthcare Ltd., Research and Collaborations GB & I, Frimley, United Kingdom
| | - Nikola Sprigg
- Stroke Trials Unit, Mental Health and Clinical Neuroscience, University of Nottingham, Nottingham, United Kingdom
| | - Fidel Alfaro-Almagro
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
| | - Mark Jenkinson
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
- South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA, Australia
- Australian Institute for Machine Learning (AIML), School of Computer Science, The University of Adelaide, Adelaide, SA, Australia
| | - Ludovica Griffanti
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
- Department of Psychiatry, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, University of Oxford, Oxford, United Kingdom
- *Correspondence: Ludovica Griffanti
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11
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Griffanti L, Gillis G, O'Donoghue MC, Blane J, Pretorius PM, Mitchell R, Aikin N, Lindsay K, Campbell J, Semple J, Alfaro-Almagro F, Smith SM, Miller KL, Martos L, Raymont V, Mackay CE. Adapting UK Biobank imaging for use in a routine memory clinic setting: The Oxford Brain Health Clinic. Neuroimage Clin 2022; 36:103273. [PMID: 36451375 PMCID: PMC9723313 DOI: 10.1016/j.nicl.2022.103273] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/24/2022] [Accepted: 11/20/2022] [Indexed: 11/23/2022]
Abstract
The Oxford Brain Health Clinic (BHC) is a joint clinical-research service that provides memory clinic patients and clinicians access to high-quality assessments not routinely available, including brain MRI aligned with the UK Biobank imaging study (UKB). In this work we present how we 1) adapted the UKB MRI acquisition protocol to be suitable for memory clinic patients, 2) modified the imaging analysis pipeline to extract measures that are in line with radiology reports and 3) explored the alignment of measures from BHC patients to the largest brain MRI study in the world (ultimately 100,000 participants). Adaptations of the UKB acquisition protocol for BHC patients include dividing the scan into core and optional sequences (i.e., additional imaging modalities) to improve patients' tolerance for the MRI assessment. We adapted the UKB structural MRI analysis pipeline to take into account the characteristics of a memory clinic population (e.g., high amount of white matter hyperintensities and hippocampal atrophy). We then compared the imaging derived phenotypes (IDPs) extracted from the structural scans to visual ratings from radiology reports, non-imaging factors (age, cognition) and to reference distributions derived from UKB data. Of the first 108 BHC attendees (August 2020-November 2021), 92.5 % completed the clinical scans, 88.0 % consented to use of data for research, and 43.5 % completed the additional research sequences, demonstrating that the protocol is well tolerated. The high rates of consent to research makes this a valuable real-world quality research dataset routinely captured in a clinical service. Modified tissue-type segmentation with lesion masking greatly improved grey matter volume estimation. CSF-masking marginally improved hippocampal segmentation. The IDPs were in line with radiology reports and showed significant associations with age and cognitive performance, in line with the literature. Due to the age difference between memory clinic patients of the BHC (age range 65-101 years, average 78.3 years) and UKB participants (44-82 years, average 64 years), additional scans on elderly healthy controls are needed to improve reference distributions. Current and future work aims to integrate automated quantitative measures in the radiology reports and evaluate their clinical utility.
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Affiliation(s)
- Ludovica Griffanti
- Department of Psychiatry, University of Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom; Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom; Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom.
| | - Grace Gillis
- Department of Psychiatry, University of Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom; Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
| | - M Clare O'Donoghue
- Department of Psychiatry, University of Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom; Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
| | - Jasmine Blane
- Department of Psychiatry, University of Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom
| | - Pieter M Pretorius
- Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom; Oxford University Hospitals NHS Trust, Oxford, United Kingdom
| | | | - Nicola Aikin
- Department of Psychiatry, University of Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom; Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
| | - Karen Lindsay
- Department of Psychiatry, University of Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom
| | - Jon Campbell
- Department of Psychiatry, University of Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom; Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
| | - Juliet Semple
- Department of Psychiatry, University of Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom; Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
| | - Fidel Alfaro-Almagro
- Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom; Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
| | - Stephen M Smith
- Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom; Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
| | - Karla L Miller
- Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom; Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
| | - Lola Martos
- Department of Psychiatry, University of Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom
| | - Vanessa Raymont
- Department of Psychiatry, University of Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom
| | - Clare E Mackay
- Department of Psychiatry, University of Oxford, United Kingdom; Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
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Griffanti L, Raman B, Alfaro-Almagro F, Filippini N, Cassar MP, Sheerin F, Okell TW, Kennedy McConnell FA, Chappell MA, Wang C, Arthofer C, Lange FJ, Andersson J, Mackay CE, Tunnicliffe EM, Rowland M, Neubauer S, Miller KL, Jezzard P, Smith SM. Adapting the UK Biobank Brain Imaging Protocol and Analysis Pipeline for the C-MORE Multi-Organ Study of COVID-19 Survivors. Front Neurol 2021; 12:753284. [PMID: 34777224 PMCID: PMC8586081 DOI: 10.3389/fneur.2021.753284] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 10/06/2021] [Indexed: 01/08/2023] Open
Abstract
SARS-CoV-2 infection has been shown to damage multiple organs, including the brain. Multiorgan MRI can provide further insight on the repercussions of COVID-19 on organ health but requires a balance between richness and quality of data acquisition and total scan duration. We adapted the UK Biobank brain MRI protocol to produce high-quality images while being suitable as part of a post-COVID-19 multiorgan MRI exam. The analysis pipeline, also adapted from UK Biobank, includes new imaging-derived phenotypes (IDPs) designed to assess the possible effects of COVID-19. A first application of the protocol and pipeline was performed in 51 COVID-19 patients post-hospital discharge and 25 controls participating in the Oxford C-MORE study. The protocol acquires high resolution T1, T2-FLAIR, diffusion weighted images, susceptibility weighted images, and arterial spin labelling data in 17 min. The automated imaging pipeline derives 1,575 IDPs, assessing brain anatomy (including olfactory bulb volume and intensity) and tissue perfusion, hyperintensities, diffusivity, and susceptibility. In the C-MORE data, IDPs related to atrophy, small vessel disease and olfactory bulbs were consistent with clinical radiology reports. Our exploratory analysis tentatively revealed some group differences between recovered COVID-19 patients and controls, across severity groups, but not across anosmia groups. Follow-up imaging in the C-MORE study is currently ongoing, and this protocol is now being used in other large-scale studies. The protocol, pipeline code and data are openly available and will further contribute to the understanding of the medium to long-term effects of COVID-19.
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Affiliation(s)
- Ludovica Griffanti
- Department of Psychiatry, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Betty Raman
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford Biomedical Research Centre (BRC) National Institute for Health Research (NIHR), University of Oxford, Oxford, United Kingdom
- Radcliffe Department of Medicine, British Heart Foundation Centre for Research Excellence, University of Oxford, Oxford, United Kingdom
| | - Fidel Alfaro-Almagro
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Nicola Filippini
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Camillo Hospital, Venice, Italy
| | - Mark Philip Cassar
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford Biomedical Research Centre (BRC) National Institute for Health Research (NIHR), University of Oxford, Oxford, United Kingdom
| | - Fintan Sheerin
- Department of Radiology, Oxford University Hospitals National Health Service (NHS) Foundation Trust, Oxford, United Kingdom
| | - Thomas W. Okell
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Flora A. Kennedy McConnell
- Mental Health & Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Nottingham Biomedical Research Centre, Queens Medical Centre, University of Nottingham, Nottingham, United Kingdom
| | - Michael A. Chappell
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
- Mental Health & Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Nottingham Biomedical Research Centre, Queens Medical Centre, University of Nottingham, Nottingham, United Kingdom
| | - Chaoyue Wang
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Christoph Arthofer
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Frederik J. Lange
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Jesper Andersson
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Clare E. Mackay
- Department of Psychiatry, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, University of Oxford, Oxford, United Kingdom
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Elizabeth M. Tunnicliffe
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford Biomedical Research Centre (BRC) National Institute for Health Research (NIHR), University of Oxford, Oxford, United Kingdom
| | - Matthew Rowland
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford Biomedical Research Centre (BRC) National Institute for Health Research (NIHR), University of Oxford, Oxford, United Kingdom
- Radcliffe Department of Medicine, British Heart Foundation Centre for Research Excellence, University of Oxford, Oxford, United Kingdom
| | - Karla L. Miller
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Peter Jezzard
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Stephen M. Smith
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
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Smith SM, Douaud G, Chen W, Hanayik T, Alfaro-Almagro F, Sharp K, Elliott LT. An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank. Nat Neurosci 2021; 24:737-745. [PMID: 33875891 PMCID: PMC7610742 DOI: 10.1038/s41593-021-00826-4] [Citation(s) in RCA: 144] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 02/23/2021] [Indexed: 02/01/2023]
Abstract
UK Biobank is a major prospective epidemiological study, including multimodal brain imaging, genetics and ongoing health outcomes. Previously, we published genome-wide associations of 3,144 brain imaging-derived phenotypes, with a discovery sample of 8,428 individuals. Here we present a new open resource of genome-wide association study summary statistics, using the 2020 data release, almost tripling the discovery sample size. We now include the X chromosome and new classes of imaging-derived phenotypes (subcortical volumes and tissue contrast). Previously, we found 148 replicated clusters of associations between genetic variants and imaging phenotypes; in this study, we found 692, including 12 on the X chromosome. We describe some of the newly found associations, focusing on the X chromosome and autosomal associations involving the new classes of imaging-derived phenotypes. Our novel associations implicate, for example, pathways involved in the rare X-linked STAR (syndactyly, telecanthus and anogenital and renal malformations) syndrome, Alzheimer's disease and mitochondrial disorders.
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Affiliation(s)
- Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Gwenaëlle Douaud
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Winfield Chen
- Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby BC, Canada
| | - Taylor Hanayik
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | | | - Lloyd T Elliott
- Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby BC, Canada.
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14
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Raman B, Cassar MP, Tunnicliffe EM, Filippini N, Griffanti L, Alfaro-Almagro F, Okell T, Sheerin F, Xie C, Mahmod M, Mózes FE, Lewandowski AJ, Ohuma EO, Holdsworth D, Lamlum H, Woodman MJ, Krasopoulos C, Mills R, McConnell FAK, Wang C, Arthofer C, Lange FJ, Andersson J, Jenkinson M, Antoniades C, Channon KM, Shanmuganathan M, Ferreira VM, Piechnik SK, Klenerman P, Brightling C, Talbot NP, Petousi N, Rahman NM, Ho LP, Saunders K, Geddes JR, Harrison PJ, Pattinson K, Rowland MJ, Angus BJ, Gleeson F, Pavlides M, Koychev I, Miller KL, Mackay C, Jezzard P, Smith SM, Neubauer S. Medium-term effects of SARS-CoV-2 infection on multiple vital organs, exercise capacity, cognition, quality of life and mental health, post-hospital discharge. EClinicalMedicine 2021; 31:100683. [PMID: 33490928 PMCID: PMC7808914 DOI: 10.1016/j.eclinm.2020.100683] [Citation(s) in RCA: 342] [Impact Index Per Article: 114.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 11/22/2020] [Accepted: 11/26/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The medium-term effects of Coronavirus disease (COVID-19) on organ health, exercise capacity, cognition, quality of life and mental health are poorly understood. METHODS Fifty-eight COVID-19 patients post-hospital discharge and 30 age, sex, body mass index comorbidity-matched controls were enrolled for multiorgan (brain, lungs, heart, liver and kidneys) magnetic resonance imaging (MRI), spirometry, six-minute walk test, cardiopulmonary exercise test (CPET), quality of life, cognitive and mental health assessments. FINDINGS At 2-3 months from disease-onset, 64% of patients experienced breathlessness and 55% reported fatigue. On MRI, abnormalities were seen in lungs (60%), heart (26%), liver (10%) and kidneys (29%). Patients exhibited changes in the thalamus, posterior thalamic radiations and sagittal stratum on brain MRI and demonstrated impaired cognitive performance, specifically in the executive and visuospatial domains. Exercise tolerance (maximal oxygen consumption and ventilatory efficiency on CPET) and six-minute walk distance were significantly reduced. The extent of extra-pulmonary MRI abnormalities and exercise intolerance correlated with serum markers of inflammation and acute illness severity. Patients had a higher burden of self-reported symptoms of depression and experienced significant impairment in all domains of quality of life compared to controls (p<0.0001 to 0.044). INTERPRETATION A significant proportion of patients discharged from hospital reported symptoms of breathlessness, fatigue, depression and had limited exercise capacity. Persistent lung and extra-pulmonary organ MRI findings are common in patients and linked to inflammation and severity of acute illness. FUNDING NIHR Oxford and Oxford Health Biomedical Research Centres, British Heart Foundation Centre for Research Excellence, UKRI, Wellcome Trust, British Heart Foundation.
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Affiliation(s)
- Betty Raman
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
- Radcliffe Department of Medicine, British Heart Foundation Centre for Research Excellence, University of Oxford, Oxford, United Kingdom
| | - Mark Philip Cassar
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
| | - Elizabeth M. Tunnicliffe
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
| | - Nicola Filippini
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Ludovica Griffanti
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Thomas Okell
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Fintan Sheerin
- Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Cheng Xie
- Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Masliza Mahmod
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
| | - Ferenc E. Mózes
- Oxford Centre for Clinical Magnetic Resonance Research, Radcliffe Department of Medicine, John Radcliffe Hospital Oxford, University of Oxford, Oxford, United Kingdom
| | - Adam J. Lewandowski
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
| | - Eric O. Ohuma
- Maternal, Adolescent, Reproductive & Child Health (MARCH) Centre, London School of Hygiene & Tropical Medicine (LSHTM), London, United Kingdom
| | - David Holdsworth
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
| | - Hanan Lamlum
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
| | - Myles J. Woodman
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
| | - Catherine Krasopoulos
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
| | - Rebecca Mills
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
| | - Flora A. Kennedy McConnell
- Division of Clinical Neuroscience, Faculty of Medicine & Health Sciences, University of Nottingham, Nottingham, United Kingdom
| | - Chaoyue Wang
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Christoph Arthofer
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Frederik J. Lange
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jesper Andersson
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Charalambos Antoniades
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
- Radcliffe Department of Medicine, British Heart Foundation Centre for Research Excellence, University of Oxford, Oxford, United Kingdom
| | - Keith M. Channon
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
- Radcliffe Department of Medicine, British Heart Foundation Centre for Research Excellence, University of Oxford, Oxford, United Kingdom
| | - Mayooran Shanmuganathan
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
| | - Vanessa M. Ferreira
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
| | - Stefan K. Piechnik
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
| | - Paul Klenerman
- Nuffield Department of Medicine, NIHR Oxford BRC, University of Oxford, Oxford, United Kingdom
| | - Christopher Brightling
- Institute for Lung Health, Department of Respiratory Sciences, NIHR Leicester BRC, University of Leicester, Leicester, United Kingdom
| | - Nick P. Talbot
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
| | - Nayia Petousi
- Nuffield Department of Medicine, NIHR Oxford BRC, University of Oxford, Oxford, United Kingdom
| | - Najib M. Rahman
- Nuffield Department of Medicine, NIHR Oxford BRC, University of Oxford, Oxford, United Kingdom
| | - Ling-Pei Ho
- Weatherall Institute of Molecular Medicine, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Kate Saunders
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - John R. Geddes
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Paul J. Harrison
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Kyle Pattinson
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
| | - Matthew J. Rowland
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
| | - Brian J. Angus
- Experimental Medicine, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Fergus Gleeson
- Department of Oncology, Medical Science Department, University of Oxford, Oxford, United Kingdom
| | - Michael Pavlides
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
- Translational Gastroenterology Unit, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Ivan Koychev
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Clare Mackay
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
| | - Peter Jezzard
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Stephen M. Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
- Radcliffe Department of Medicine, British Heart Foundation Centre for Research Excellence, University of Oxford, Oxford, United Kingdom
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15
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Veldsman M, Nobis L, Alfaro-Almagro F, Manohar S, Husain M. The human hippocampus and its subfield volumes across age, sex and APOE e4 status. Brain Commun 2020; 3:fcaa219. [PMID: 33615215 PMCID: PMC7884607 DOI: 10.1093/braincomms/fcaa219] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 11/04/2020] [Accepted: 11/16/2020] [Indexed: 01/03/2023] Open
Abstract
Female sex, age and carriage of the apolipoprotein E e4 allele are the greatest risk factors for sporadic Alzheimer's disease. The hippocampus has a selective vulnerability to atrophy in ageing that may be accelerated in Alzheimer's disease, including in those with increased genetic risk of the disease, years before onset. Within the hippocampal complex, subfields represent cytoarchitectonic and connectivity based divisions. Variation in global hippocampal and subfield volume associated with sex, age and apolipoprotein E e4 status has the potential to provide a sensitive biomarker of future vulnerability to Alzheimer's disease. Here, we examined non-linear age, sex and apolipoprotein E effects, and their interactions, on hippocampal and subfield volumes across several decades spanning mid-life to old age in 36 653 healthy ageing individuals. FMRIB Software Library derived estimates of total hippocampal volume and Freesurfer derived estimates hippocampal subfield volume were estimated. A model-free, sliding-window approach was implemented that does not assume a linear relationship between age and subfield volume. The annualized percentage of subfield volume change was calculated to investigate associations with age, sex and apolipoprotein E e4 homozygosity. Hippocampal volume showed a marked reduction in apolipoprotein E e4/e4 female carriers after age 65. Volume was lower in homozygous e4 individuals in specific subfields including the presubiculum, subiculum head, cornu ammonis 1 body, cornu ammonis 3 head and cornu ammonis 4. Nearby brain structures in medial temporal and subcortical regions did not show the same age, sex and apolipoprotein E interactions, suggesting selective vulnerability of the hippocampus and its subfields. The findings demonstrate that in healthy ageing, two factors-female sex and apolipoprotein E e4 status-confer selective vulnerability of specific hippocampal subfields to volume loss.
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Affiliation(s)
- Michele Veldsman
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Lisa Nobis
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Experimental Psychology, University of Oxford, Oxford, UK
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK
| | | | - Sanjay Manohar
- Department of Experimental Psychology, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neuroscience, John Radcliffe Hospital, University of Oxford, Oxford, UK
- Division of Clinical Neurology, John Radcliffe Hospital, Oxford University Hospitals Trust, Oxford, UK
| | - Masud Husain
- Department of Experimental Psychology, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neuroscience, John Radcliffe Hospital, University of Oxford, Oxford, UK
- Division of Clinical Neurology, John Radcliffe Hospital, Oxford University Hospitals Trust, Oxford, UK
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16
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Zarnani K, Smith SM, Alfaro-Almagro F, Fagerlund B, Lauritzen M, Rostrup E, Nichols TE. Discovering correlates of age-related decline in a healthy late-midlife male birth cohort. Aging (Albany NY) 2020; 12:16709-16743. [PMID: 32913141 PMCID: PMC7521526 DOI: 10.18632/aging.103345] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 05/01/2020] [Indexed: 01/24/2023]
Abstract
Studies exploring age-related brain and cognitive change have identified substantial heterogeneity among individuals, but the underlying reasons for the differential trajectories remain largely unknown. We investigated cross-sectional and longitudinal associations between brain-imaging phenotypes (IDPs) and cognitive ability, and how these relations may be modified by common risk and protective factors. Participants were recruited from the 1953 Danish Male Birth Cohort (N=123), a longitudinal study of cognitive and brain ageing. Childhood IQ and socio-demographic factors are available for these participants who have been assessed regularly on multiple IDPs and behavioural factors in midlife. Using Pearson correlations and canonical correlation analysis (CCA), we explored the relation between 454 IDPs and 114 behavioural variables. CCA identified a single mode of population covariation coupling cross-subject longitudinal changes in brain structure to changes in cognitive performance and to a range of age-related covariates (r=0.92, Pcorrected < 0.001). Specifically, this CCA-mode indicated that; decreases in IQ and speed assessed tasks, higher rates of familial myocardial infarct, less physical activity, and poorer mental health are associated with larger decreases in whole brain grey matter and white matter. We found no evidence supporting the role of baseline scores as predictors of impending brain and behavioural change in late-midlife.
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Affiliation(s)
- Kiyana Zarnani
- Functional Imaging Unit, Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital Rigshospitalet, Glostrup, Denmark
- Center for Healthy Aging, University of Copenhagen, Copenhagen, Denmark
- Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Stephen M. Smith
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Birgitte Fagerlund
- Center for Neuropsychiatric Schizophrenia Research, Mental Health Center Glostrup, Denmark
- Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Martin Lauritzen
- Center for Healthy Aging, University of Copenhagen, Copenhagen, Denmark
- Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Neurophysiology, Rigshospitalet-Glostrup, Denmark
| | - Egill Rostrup
- Center for Healthy Aging, University of Copenhagen, Copenhagen, Denmark
- Center for Neuropsychiatric Schizophrenia Research, Mental Health Center Glostrup, Denmark
| | - Thomas E. Nichols
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Oxford Big Data Institute, Li Ka Shing, Centre For Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, UK
- Department of Statistics, University of Warwick, Coventry, UK
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17
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Armstrong NJ, Mather KA, Sargurupremraj M, Knol MJ, Malik R, Satizabal CL, Yanek LR, Wen W, Gudnason VG, Dueker ND, Elliott LT, Hofer E, Bis J, Jahanshad N, Li S, Logue MA, Luciano M, Scholz M, Smith AV, Trompet S, Vojinovic D, Xia R, Alfaro-Almagro F, Ames D, Amin N, Amouyel P, Beiser AS, Brodaty H, Deary IJ, Fennema-Notestine C, Gampawar PG, Gottesman R, Griffanti L, Jack CR, Jenkinson M, Jiang J, Kral BG, Kwok JB, Lampe L, C M Liewald D, Maillard P, Marchini J, Bastin ME, Mazoyer B, Pirpamer L, Rafael Romero J, Roshchupkin GV, Schofield PR, Schroeter ML, Stott DJ, Thalamuthu A, Trollor J, Tzourio C, van der Grond J, Vernooij MW, Witte VA, Wright MJ, Yang Q, Morris Z, Siggurdsson S, Psaty B, Villringer A, Schmidt H, Haberg AK, van Duijn CM, Jukema JW, Dichgans M, Sacco RL, Wright CB, Kremen WS, Becker LC, Thompson PM, Mosley TH, Wardlaw JM, Ikram MA, Adams HHH, Seshadri S, Sachdev PS, Smith SM, Launer L, Longstreth W, DeCarli C, Schmidt R, Fornage M, Debette S, Nyquist PA. Common Genetic Variation Indicates Separate Causes for Periventricular and Deep White Matter Hyperintensities. Stroke 2020; 51:2111-2121. [PMID: 32517579 DOI: 10.1161/strokeaha.119.027544] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
BACKGROUND AND PURPOSE Periventricular white matter hyperintensities (WMH; PVWMH) and deep WMH (DWMH) are regional classifications of WMH and reflect proposed differences in cause. In the first study, to date, we undertook genome-wide association analyses of DWMH and PVWMH to show that these phenotypes have different genetic underpinnings. METHODS Participants were aged 45 years and older, free of stroke and dementia. We conducted genome-wide association analyses of PVWMH and DWMH in 26,654 participants from CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology), ENIGMA (Enhancing Neuro-Imaging Genetics Through Meta-Analysis), and the UKB (UK Biobank). Regional correlations were investigated using the genome-wide association analyses -pairwise method. Cross-trait genetic correlations between PVWMH, DWMH, stroke, and dementia were estimated using LDSC. RESULTS In the discovery and replication analysis, for PVWMH only, we found associations on chromosomes 2 (NBEAL), 10q23.1 (TSPAN14/FAM231A), and 10q24.33 (SH3PXD2A). In the much larger combined meta-analysis of all cohorts, we identified ten significant regions for PVWMH: chromosomes 2 (3 regions), 6, 7, 10 (2 regions), 13, 16, and 17q23.1. New loci of interest include 7q36.1 (NOS3) and 16q24.2. In both the discovery/replication and combined analysis, we found genome-wide significant associations for the 17q25.1 locus for both DWMH and PVWMH. Using gene-based association analysis, 19 genes across all regions were identified for PVWMH only, including the new genes: CALCRL (2q32.1), KLHL24 (3q27.1), VCAN (5q27.1), and POLR2F (22q13.1). Thirteen genes in the 17q25.1 locus were significant for both phenotypes. More extensive genetic correlations were observed for PVWMH with small vessel ischemic stroke. There were no associations with dementia for either phenotype. CONCLUSIONS Our study confirms these phenotypes have distinct and also shared genetic architectures. Genetic analyses indicated PVWMH was more associated with ischemic stroke whilst DWMH loci were implicated in vascular, astrocyte, and neuronal function. Our study confirms these phenotypes are distinct neuroimaging classifications and identifies new candidate genes associated with PVWMH only.
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Affiliation(s)
- Nicola J Armstrong
- Mathematics and Statistics, Murdoch University, Perth, Australia (N.J.A.)
| | - Karen A Mather
- Centre for Healthy Brain Ageing, School of Psychiatry (K.A.M., W.W., H.B., J.J., A.T., J.T., P.S.S.), University of New South Wales, Sydney, Australia.,Neuroscience Research Australia, Sydney, Australia (K.A.M., P.R.S., A.T.)
| | | | - Maria J Knol
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands (M.J.K., D.V., N.A., G.V.R., M.W.V., C.M.v.D., M.A.I., H.H.H.A.)
| | - Rainer Malik
- Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig-Maximilians-Universität LMU Munich, Germany (R.M., M.D.)
| | - Claudia L Satizabal
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX (C.L.S., S.S.).,The Framingham Heart Study, MA (C.L.S., A.S.B., J.R.R., S.S.).,Department of Neurology (C.L.S., A.S.B., J.R.R., S.S.), Boston University School of Medicine, MA
| | - Lisa R Yanek
- GeneSTAR Research Program (L.R.Y., B.G.K., L.C.B., P.A.N.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Wei Wen
- Centre for Healthy Brain Ageing, School of Psychiatry (K.A.M., W.W., H.B., J.J., A.T., J.T., P.S.S.), University of New South Wales, Sydney, Australia
| | - Vilmundur G Gudnason
- Icelandic Heart Association, Kopavogur (V.G.G., S.S.).,University of Iceland, Reykjavik, Iceland (V.G.G., A.V.S.)
| | - Nicole D Dueker
- Dr. John T. Macdonald Foundation Department of Human Genetics (R.L.S.), University of Miami, FL
| | - Lloyd T Elliott
- Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, Canada (L.T.E.).,Wellcome Centre for Integrative Neuroimaging (WIN FMRIB) (L.T.E., F.A.-A., L.G., M.J., S.M.S.), University of Oxford, United Kingdom
| | - Edith Hofer
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Austria (E.H., R.S.).,Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria (E.H.)
| | - Joshua Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA (J.B., B.P., W.L.)
| | - Neda Jahanshad
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey (N.J., P.M.T.)
| | - Shuo Li
- Department of Biostatistics, Boston University School of Public Health, Boston, MA (S.L., M.A.L., A.S.B., Q.Y.)
| | - Mark A Logue
- Department of Psychiatry and Biomedical Genetics Section (M.A.L.), Boston University School of Medicine, MA.,Department of Biostatistics, Boston University School of Public Health, Boston, MA (S.L., M.A.L., A.S.B., Q.Y.).,National Center for PTSD: Behavioral Science Division, VA Boston Healthcare System, Boston, MA (M.A.L.)
| | - Michelle Luciano
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, United Kingdom (M.L., I.J.D., D.C.M.L., M.E.B., J.M.W.)
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology (M.S.)
| | - Albert V Smith
- University of Iceland, Reykjavik, Iceland (V.G.G., A.V.S.)
| | - Stella Trompet
- Department of Internal Medicine, Section of Gerontology and Geriatrics (S.T.), Leiden University Medical Center, the Netherlands.,Department of Cardiology (S.T.), Leiden University Medical Center, the Netherlands
| | - Dina Vojinovic
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands (M.J.K., D.V., N.A., G.V.R., M.W.V., C.M.v.D., M.A.I., H.H.H.A.)
| | - Rui Xia
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, TX (R.X., M.F.)
| | - Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB) (L.T.E., F.A.-A., L.G., M.J., S.M.S.), University of Oxford, United Kingdom
| | - David Ames
- National Ageing Research Institute, Parkville, Victoria, Australia (D.A.).,Academic Unit for Psychiatry of Old Age, University of Melbourne, St George's Hospital, Kew, Australia (D.A.)
| | - Najaf Amin
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands (M.J.K., D.V., N.A., G.V.R., M.W.V., C.M.v.D., M.A.I., H.H.H.A.)
| | - Philippe Amouyel
- Lille University, Inserm, Institut Pasteur de Lille, RID-AGE - Risk Factors and Molecular Determinants of Aging-Related Diseases and Labex Distalz, France (P.A.).,Lille University, Inserm, CHU Lille, Institut Pasteur de Lille, RID-AGE (P.A.)
| | - Alexa S Beiser
- The Framingham Heart Study, MA (C.L.S., A.S.B., J.R.R., S.S.).,Department of Neurology (C.L.S., A.S.B., J.R.R., S.S.), Boston University School of Medicine, MA.,Department of Biostatistics, Boston University School of Public Health, Boston, MA (S.L., M.A.L., A.S.B., Q.Y.)
| | - Henry Brodaty
- Centre for Healthy Brain Ageing, School of Psychiatry (K.A.M., W.W., H.B., J.J., A.T., J.T., P.S.S.), University of New South Wales, Sydney, Australia.,Dementia Centre for Research Collaboration (H.B.), University of New South Wales, Sydney, Australia
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, United Kingdom (M.L., I.J.D., D.C.M.L., M.E.B., J.M.W.)
| | - Christine Fennema-Notestine
- Department of Psychiatry (C.F.-N.), University of California, San Diego, La Jolla, CA.,Center for Behavior Genetics of Aging (C.F.-N.), University of California, San Diego, La Jolla, CA
| | - Piyush G Gampawar
- Gottfried Schatz Research Center (for Cell Signaling, Metabolism and Aging), Medical University of Graz, Austria (P.G.G., H.S.)
| | - Rebecca Gottesman
- Department of Neurology, Cerebrovascular and stroke Division (R.G.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Ludovica Griffanti
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB) (L.T.E., F.A.-A., L.G., M.J., S.M.S.), University of Oxford, United Kingdom
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN (C.R.J.J.)
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB) (L.T.E., F.A.-A., L.G., M.J., S.M.S.), University of Oxford, United Kingdom
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing, School of Psychiatry (K.A.M., W.W., H.B., J.J., A.T., J.T., P.S.S.), University of New South Wales, Sydney, Australia
| | - Brian G Kral
- GeneSTAR Research Program (L.R.Y., B.G.K., L.C.B., P.A.N.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - John B Kwok
- School of Medical Sciences (J.B.K., P.R.S.), University of New South Wales, Sydney, Australia.,Brain and Mind Centre - The University of Sydney, Camperdown, NSW, Australia (J.B.K.)
| | - Leonie Lampe
- Department of Neurology, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (L.L., V.A.W.)
| | - David C M Liewald
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, United Kingdom (M.L., I.J.D., D.C.M.L., M.E.B., J.M.W.)
| | - Pauline Maillard
- Imaging of Dementia and Aging (IDeA) Laboratory, Department of Neurology, University of California-Davis, Davis, CA (P.M.)
| | - Jonathan Marchini
- Statistical Genetics and Methods at Regeneron Pharmaceuticals, Inc, New York, NY (J.M.)
| | - Mark E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, United Kingdom (M.L., I.J.D., D.C.M.L., M.E.B., J.M.W.).,Centre for Clinical Brain Sciences, Edinburgh Imaging, Centre for Cognitive Ageing, University of Edinburgh, United Kingdom (M.E.B., J.M.W.)
| | - Bernard Mazoyer
- Institut des Maladies Neurodégénératives, University of Bordeaux, France (B.M.)
| | - Lukas Pirpamer
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Austria (L.P.)
| | - José Rafael Romero
- The Framingham Heart Study, MA (C.L.S., A.S.B., J.R.R., S.S.).,Department of Neurology (C.L.S., A.S.B., J.R.R., S.S.), Boston University School of Medicine, MA
| | - Gennady V Roshchupkin
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands (M.J.K., D.V., N.A., G.V.R., M.W.V., C.M.v.D., M.A.I., H.H.H.A.).,Department of Radiology and Nuclear Medicine (G.V.R., M.W.V., H.H.H.A.)
| | - Peter R Schofield
- School of Medical Sciences (J.B.K., P.R.S.), University of New South Wales, Sydney, Australia.,Neuroscience Research Australia, Sydney, Australia (K.A.M., P.R.S., A.T.)
| | - Matthias L Schroeter
- LIFE Research Center for Civilization Disease, Leipzig, Germany (M.S.).,Department of Neurology, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (M.L.S., A.V.).,Day Clinic for Cognitive Neurology, University Hospital Leipzig, Germany (M.L.S., A.V.)
| | - David J Stott
- Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, United Kingdom (D.J.S.)
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing, School of Psychiatry (K.A.M., W.W., H.B., J.J., A.T., J.T., P.S.S.), University of New South Wales, Sydney, Australia.,Neuroscience Research Australia, Sydney, Australia (K.A.M., P.R.S., A.T.)
| | - Julian Trollor
- Centre for Healthy Brain Ageing, School of Psychiatry (K.A.M., W.W., H.B., J.J., A.T., J.T., P.S.S.), University of New South Wales, Sydney, Australia.,Department of Developmental Disability Neuropsychiatry, School of Psychiatry (J.T.), University of New South Wales, Sydney, Australia
| | - Christophe Tzourio
- University Bordeaux, Inserm, Bordeaux Population Health Research Center, France (M.S., C.T., S.D.).,CHU de Bordeaux, Public Health Department, Medical information Department, Bordeaux, France (C.T.)
| | - Jeroen van der Grond
- Department of Radiology (J.v.d.G.), Leiden University Medical Center, the Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands (M.J.K., D.V., N.A., G.V.R., M.W.V., C.M.v.D., M.A.I., H.H.H.A.).,Department of Radiology and Nuclear Medicine (G.V.R., M.W.V., H.H.H.A.)
| | - Veronica A Witte
- Collaborative Research Center 1052 Obesity Mechanisms, Faculty of Medicine, University of Leipzig, Germany (V.A.W).,Department of Neurology, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (L.L., V.A.W.)
| | - Margaret J Wright
- Queensland Brain Institute (M.J.W.), The University of Queensland, St Lucia, QLD, Australia.,Centre for Advanced Imaging (M.J.W.), The University of Queensland, St Lucia, QLD, Australia
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA (S.L., M.A.L., A.S.B., Q.Y.)
| | - Zoe Morris
- Neuroradiology Department, Department of Clinical Neurosciences, Western General Hospital, Edinburgh, United Kingdom (Z.M.)
| | - Siggi Siggurdsson
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX (C.L.S., S.S.).,The Framingham Heart Study, MA (C.L.S., A.S.B., J.R.R., S.S.).,Department of Neurology (C.L.S., A.S.B., J.R.R., S.S.), Boston University School of Medicine, MA
| | - Bruce Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA (J.B., B.P., W.L.)
| | - Arno Villringer
- Department of Neurology, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (M.L.S., A.V.).,Day Clinic for Cognitive Neurology, University Hospital Leipzig, Germany (M.L.S., A.V.)
| | - Helena Schmidt
- Gottfried Schatz Research Center (for Cell Signaling, Metabolism and Aging), Medical University of Graz, Austria (P.G.G., H.S.)
| | - Asta K Haberg
- Department of Neuromedicine and Movement Science (A.K.H.), Norwegian University of Science and Technology, Trondheim, Norway.,Department of Radiology and Nuclear Medicine (A.K.H.), Norwegian University of Science and Technology, Trondheim, Norway
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands (M.J.K., D.V., N.A., G.V.R., M.W.V., C.M.v.D., M.A.I., H.H.H.A.).,Nuffield Department of Population Health (C.M.v.D.), University of Oxford, United Kingdom
| | - J Wouter Jukema
- Department of Cardiology (J.W.J.), Leiden University Medical Center, the Netherlands.,Einthoven Laboratory for Experimental Vascular Medicine, LUMC, Leiden, the Netherlands (J.W.J.)
| | - Martin Dichgans
- Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig-Maximilians-Universität LMU Munich, Germany (R.M., M.D.).,German Center for Neurodegenerative Diseases, Munich, Germany (M.D.).,Munich Cluster for Systems Neurology (SyNergy), Germany (M.D.)
| | - Ralph L Sacco
- Department of Public Health Sciences, Miller School of Medicine (R.L.S.), University of Miami, FL.,Department of Neurology, Miller School of Medicine (R.L.S.), University of Miami, FL.,Evelyn F. McKnight Brain Institute, Department of Neurology (R.L.S.), University of Miami, FL
| | - Clinton B Wright
- National Institute of Neurological Disorders and Stroke (C.B.W.), National Institutes of Health, Bethesda, MD
| | - William S Kremen
- Center for Behavior Genetics of Aging (W.S.K.), University of California, San Diego, La Jolla, CA.,Department of Psychiatry (W.S.K.), University of California, San Diego, La Jolla, CA
| | - Lewis C Becker
- GeneSTAR Research Program (L.R.Y., B.G.K., L.C.B., P.A.N.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Paul M Thompson
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey (N.J., P.M.T.)
| | - Thomas H Mosley
- Department of Geriatric Medicine, Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson (T.H.M.)
| | - Joanna M Wardlaw
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, United Kingdom (M.L., I.J.D., D.C.M.L., M.E.B., J.M.W.).,Centre for Clinical Brain Sciences, Edinburgh Imaging, Centre for Cognitive Ageing, University of Edinburgh, United Kingdom (M.E.B., J.M.W.)
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands (M.J.K., D.V., N.A., G.V.R., M.W.V., C.M.v.D., M.A.I., H.H.H.A.)
| | - Hieab H H Adams
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands (M.J.K., D.V., N.A., G.V.R., M.W.V., C.M.v.D., M.A.I., H.H.H.A.).,Department of Radiology and Nuclear Medicine (G.V.R., M.W.V., H.H.H.A.).,Department of Clinical Genetics, Erasmus MC, Rotterdam, the Netherlands (H.H.H.A.)
| | | | - Perminder S Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry (K.A.M., W.W., H.B., J.J., A.T., J.T., P.S.S.), University of New South Wales, Sydney, Australia.,Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, Australia (P.S.S.)
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB) (L.T.E., F.A.-A., L.G., M.J., S.M.S.), University of Oxford, United Kingdom
| | - Lenore Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program (L.L.), National Institutes of Health, Bethesda, MD
| | - William Longstreth
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA (J.B., B.P., W.L.)
| | - Charles DeCarli
- Alzheimer's Disease Center and Imaging of Dementia and Aging (IDeA) Laboratory, Department of Neurology and Center for Neuroscience University of California at Davis (C.D.)
| | - Reinhold Schmidt
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Austria (E.H., R.S.)
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, TX (R.X., M.F.).,Human Genetics Center, School of Public Health UT, Houston, TX (M.F.)
| | - Stephanie Debette
- University Bordeaux, Inserm, Bordeaux Population Health Research Center, France (M.S., C.T., S.D.).,Department of Neurology, CHU de Bordeaux (University Hospital), Bordeaux, France (S.D.)
| | - Paul A Nyquist
- GeneSTAR Research Program (L.R.Y., B.G.K., L.C.B., P.A.N.), Johns Hopkins University School of Medicine, Baltimore, MD.,Departments of Neurology, Critical Care Medicine, Neurosurgery (P.A.N.), Johns Hopkins University School of Medicine, Baltimore, MD.,Critical Care Medicine Department (P.A.N.), National Institutes of Health, Bethesda, MD
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18
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Alfaro-Almagro F, McCarthy P, Afyouni S, Andersson JLR, Bastiani M, Miller KL, Nichols TE, Smith SM. Confound modelling in UK Biobank brain imaging. Neuroimage 2020; 224:117002. [PMID: 32502668 PMCID: PMC7610719 DOI: 10.1016/j.neuroimage.2020.117002] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 05/08/2020] [Accepted: 05/25/2020] [Indexed: 01/19/2023] Open
Abstract
Dealing with confounds is an essential step in large cohort studies to address problems such as unexplained variance and spurious correlations. UK Biobank is a powerful resource for studying associations between imaging and non-imaging measures such as lifestyle factors and health outcomes, in part because of the large subject numbers. However, the resulting high statistical power also raises the sensitivity to confound effects, which therefore have to be carefully considered. In this work we describe a set of possible confounds (including nonlinear effects and interactions that researchers may wish to consider for their studies using such data). We include descriptions of how we can estimate the confounds, and study the extent to which each of these confounds affects the data, and the spurious correlations that may arise if they are not controlled. Finally, we discuss several issues that future studies should consider when dealing with confounds.
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Affiliation(s)
- Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK.
| | - Paul McCarthy
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | | | - Jesper L R Andersson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK; NIHR Biomedical Research Centre, University of Nottingham, UK
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Thomas E Nichols
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Big Data Institute, University of Oxford, UK
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
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19
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Littlejohns TJ, Holliday J, Gibson LM, Garratt S, Oesingmann N, Alfaro-Almagro F, Bell JD, Boultwood C, Collins R, Conroy MC, Crabtree N, Doherty N, Frangi AF, Harvey NC, Leeson P, Miller KL, Neubauer S, Petersen SE, Sellors J, Sheard S, Smith SM, Sudlow CLM, Matthews PM, Allen NE. The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions. Nat Commun 2020; 11:2624. [PMID: 32457287 PMCID: PMC7250878 DOI: 10.1038/s41467-020-15948-9] [Citation(s) in RCA: 236] [Impact Index Per Article: 59.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Accepted: 04/03/2020] [Indexed: 01/18/2023] Open
Abstract
UK Biobank is a population-based cohort of half a million participants aged 40-69 years recruited between 2006 and 2010. In 2014, UK Biobank started the world's largest multi-modal imaging study, with the aim of re-inviting 100,000 participants to undergo brain, cardiac and abdominal magnetic resonance imaging, dual-energy X-ray absorptiometry and carotid ultrasound. The combination of large-scale multi-modal imaging with extensive phenotypic and genetic data offers an unprecedented resource for scientists to conduct health-related research. This article provides an in-depth overview of the imaging enhancement, including the data collected, how it is managed and processed, and future directions.
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Affiliation(s)
| | - Jo Holliday
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Lorna M Gibson
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
- Department of Clinical Radiology, New Royal Infirmary of Edinburgh, Edinburgh, UK
| | | | | | - Fidel Alfaro-Almagro
- Centre for Functional MRI of the Brain, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Jimmy D Bell
- Research Centre for Optimal Health, University of Westminster, London, UK
| | | | - Rory Collins
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Megan C Conroy
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Nicola Crabtree
- Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
| | | | - Alejandro F Frangi
- Department of Cardiovascular Sciences and Electrical Engineering, KU Leuven, Leuven, Belgium
- CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine, Schools of Computing and Medicine, University of Leeds, Leeds, UK
| | - Nicholas C Harvey
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - Paul Leeson
- Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Karla L Miller
- Centre for Functional MRI of the Brain, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Stefan Neubauer
- Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Steffen E Petersen
- William Harvey Research Institute, Queen Mary University of Medicine, London, UK
| | - Jonathan Sellors
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- UK Biobank Coordinating Centre, Stockport, UK
| | | | - Stephen M Smith
- Centre for Functional MRI of the Brain, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Cathie L M Sudlow
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Paul M Matthews
- Department of Brain Sciences, Imperial College London and UK Dementia Research Institute, London, UK
| | - Naomi E Allen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
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20
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Smith SM, Elliott LT, Alfaro-Almagro F, McCarthy P, Nichols TE, Douaud G, Miller KL. Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations. eLife 2020; 9:e52677. [PMID: 32134384 PMCID: PMC7162660 DOI: 10.7554/elife.52677] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 03/02/2020] [Indexed: 12/27/2022] Open
Abstract
Brain imaging can be used to study how individuals' brains are aging, compared against population norms. This can inform on aspects of brain health; for example, smoking and blood pressure can be seen to accelerate brain aging. Typically, a single 'brain age' is estimated per subject, whereas here we identified 62 modes of subject variability, from 21,407 subjects' multimodal brain imaging data in UK Biobank. The modes represent different aspects of brain aging, showing distinct patterns of functional and structural brain change, and distinct patterns of association with genetics, lifestyle, cognition, physical measures and disease. While conventional brain-age modelling found no genetic associations, 34 modes had genetic associations. We suggest that it is important not to treat brain aging as a single homogeneous process, and that modelling of distinct patterns of structural and functional change will reveal more biologically meaningful markers of brain aging in health and disease.
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Affiliation(s)
- Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of OxfordOxfordUnited Kingdom
| | - Lloyd T Elliott
- Department of Statistics and Actuarial Science, Simon Fraser UniversityVancouverCanada
| | - Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of OxfordOxfordUnited Kingdom
| | - Paul McCarthy
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of OxfordOxfordUnited Kingdom
| | - Thomas E Nichols
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of OxfordOxfordUnited Kingdom
- Big Data Institute, University of OxfordOxfordUnited Kingdom
| | - Gwenaëlle Douaud
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of OxfordOxfordUnited Kingdom
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of OxfordOxfordUnited Kingdom
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21
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Wiberg A, Ng M, Al Omran Y, Alfaro-Almagro F, McCarthy P, Marchini J, Bennett DL, Smith S, Douaud G, Furniss D. Handedness, language areas and neuropsychiatric diseases: insights from brain imaging and genetics. Brain 2019; 142:2938-2947. [PMID: 31504236 PMCID: PMC6763735 DOI: 10.1093/brain/awz257] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 07/01/2019] [Accepted: 07/04/2019] [Indexed: 12/18/2022] Open
Abstract
Ninety per cent of the human population has been right-handed since the Paleolithic, yet the brain signature and genetic basis of handedness remain poorly characterized. Here, we correlated brain imaging phenotypes from ∼9000 UK Biobank participants with handedness, and with loci found significantly associated with handedness after we performed genome-wide association studies (GWAS) in ∼400 000 of these participants. Our imaging-handedness analysis revealed an increase in functional connectivity between left and right language networks in left-handers. GWAS of handedness uncovered four significant loci (rs199512, rs45608532, rs13017199, and rs3094128), three of which are in-or expression quantitative trait loci of-genes encoding proteins involved in brain development and patterning. These included microtubule-related MAP2 and MAPT, as well as WNT3 and MICB, all implicated in the pathogenesis of diseases such as Parkinson's, Alzheimer's and schizophrenia. In particular, with rs199512, we identified a common genetic influence on handedness, psychiatric phenotypes, Parkinson's disease, and the integrity of white matter tracts connecting the same language-related regions identified in the handedness-imaging analysis. This study has identified in the general population genome-wide significant loci for human handedness in, and expression quantitative trait loci of, genes associated with brain development, microtubules and patterning. We suggest that these genetic variants contribute to neurodevelopmental lateralization of brain organization, which in turn influences both the handedness phenotype and the predisposition to develop certain neurological and psychiatric diseases.
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Affiliation(s)
- Akira Wiberg
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Science, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Michael Ng
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Science, University of Oxford, Oxford, UK
| | - Yasser Al Omran
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Science, University of Oxford, Oxford, UK
| | - Fidel Alfaro-Almagro
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Paul McCarthy
- Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | | | - David L Bennett
- Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Stephen Smith
- Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Gwenaëlle Douaud
- Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Dominic Furniss
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Science, University of Oxford, Oxford, UK
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22
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Zarnani K, Nichols TE, Alfaro-Almagro F, Fagerlund B, Lauritzen M, Rostrup E, Smith SM. Discovering markers of healthy aging: a prospective study in a Danish male birth cohort. Aging (Albany NY) 2019; 11:5943-5974. [PMID: 31480020 PMCID: PMC6738442 DOI: 10.18632/aging.102151] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Accepted: 07/31/2019] [Indexed: 01/23/2023]
Abstract
There is a pressing need to identify markers of cognitive and neural decline in healthy late-midlife participants. We explored the relationship between cross-sectional structural brain-imaging derived phenotypes (IDPs) and cognitive ability, demographic, health and lifestyle factors (non-IDPs). Participants were recruited from the 1953 Danish Male Birth Cohort (N=193). Applying an extreme group design, members were selected in 2 groups based on cognitive change between IQ at age ~20y (IQ-20) and age ~57y (IQ-57). Subjects showing the highest (n=95) and lowest (n=98) change were selected (at age ~57) for assessments on multiple IDPs and non-IDPs. We investigated the relationship between 453 IDPs and 70 non-IDPs through pairwise correlation and multivariate canonical correlation analysis (CCA) models. Significant pairwise associations included positive associations between IQ-20 and gray-matter volume of the temporal pole. CCA identified a richer pattern - a single "positive-negative" mode of population co-variation coupling individual cross-subject variations in IDPs to an extensive range of non-IDP measures (r = 0.75, Pcorrected < 0.01). Specifically, this mode linked higher cognitive performance, positive early-life social factors, and mental health to a larger brain volume of several brain structures, overall volume, and microstructural properties of some white matter tracts. Interestingly, both statistical models identified IQ-20 and gray-matter volume of the temporal pole as important contributors to the inter-individual variation observed. The converging patterns provide novel insight into the importance of early adulthood intelligence as a significant marker of late-midlife neural decline and motivates additional study.
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Affiliation(s)
- Kiyana Zarnani
- Functional Imaging Unit, Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital Rigshospitalet, Glostrup, Denmark.,Center for Healthy Aging, University of Copenhagen, Copenhagen, Denmark.,Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark.,Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Thomas E Nichols
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.,Oxford Big Data Institute, Li Ka Shing, Centre For Health Information and Discovery, Nuffield Department of Population Health University of Oxford, Oxford, UK.,Department of Statistics, University of Warwick, Coventry, UK
| | - Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Birgitte Fagerlund
- Center for Neuropsychiatric Schizophrenia Research, Mental Health Center, Glostrup, Denmark.,Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Martin Lauritzen
- Center for Healthy Aging, University of Copenhagen, Copenhagen, Denmark.,Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark.,Department of Clinical Medicine, Glostrup, Denmark
| | - Egill Rostrup
- Center for Healthy Aging, University of Copenhagen, Copenhagen, Denmark.,Center for Neuropsychiatric Schizophrenia Research, Mental Health Center, Glostrup, Denmark.,Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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23
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Nobis L, Manohar SG, Smith SM, Alfaro-Almagro F, Jenkinson M, Mackay CE, Husain M. Hippocampal volume across age: Nomograms derived from over 19,700 people in UK Biobank. Neuroimage Clin 2019; 23:101904. [PMID: 31254939 PMCID: PMC6603440 DOI: 10.1016/j.nicl.2019.101904] [Citation(s) in RCA: 91] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 06/13/2019] [Accepted: 06/16/2019] [Indexed: 12/18/2022]
Abstract
Measurement of hippocampal volume has proven useful to diagnose and track progression in several brain disorders, most notably in Alzheimer's disease (AD). For example, an objective evaluation of a patient's hippocampal volume status may provide important information that can assist diagnosis or risk stratification of AD. However, clinicians and researchers require access to age-related normative percentiles to reliably categorise a patient's hippocampal volume as being pathologically small. Here we analysed effects of age, sex, and hemisphere on the hippocampus and neighbouring temporal lobe volumes, in 19,793 generally healthy participants in the UK Biobank. A key finding of the current study is a significant acceleration in the rate of hippocampal volume loss in middle age, more pronounced in females than in males. In this report, we provide normative values for hippocampal and total grey matter volume as a function of age for reference in clinical and research settings. These normative values may be used in combination with our online, automated percentile estimation tool to provide a rapid, objective evaluation of an individual's hippocampal volume status. The data provide a large-scale normative database to facilitate easy age-adjusted determination of where an individual hippocampal and temporal lobe volume lies within the normal distribution. Largest normative database for hippocampal volume across age to date. Hippocampal volume loss accelerates in middle age. Acceleration of hippocampal volume loss is more pronounced in women than in men. With the online tool the database provides a useful resource for research and clinical studies.
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Affiliation(s)
- Lisa Nobis
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK.
| | - Sanjay G Manohar
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Oxford Health NHS Foundation Trust, Oxford, UK
| | - Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Oxford Health NHS Foundation Trust, Oxford, UK
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Oxford Health NHS Foundation Trust, Oxford, UK
| | - Clare E Mackay
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK; Oxford Health NHS Foundation Trust, Oxford, UK
| | - Masud Husain
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Department of Experimental Psychology, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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24
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Smith SM, Vidaurre D, Alfaro-Almagro F, Nichols TE, Miller KL. Estimation of brain age delta from brain imaging. Neuroimage 2019; 200:528-539. [PMID: 31201988 PMCID: PMC6711452 DOI: 10.1016/j.neuroimage.2019.06.017] [Citation(s) in RCA: 180] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 06/01/2019] [Accepted: 06/05/2019] [Indexed: 11/25/2022] Open
Abstract
It is of increasing interest to study “brain age” - the apparent age of a subject, as inferred from brain imaging data. The difference between brain age and actual age (the “delta”) is typically computed, reflecting deviation from the population norm. This therefore may reflect accelerated aging (positive delta) or resilience (negative delta) and has been found to be a useful correlate with factors such as disease and cognitive decline. However, although there has been a range of methods proposed for estimating brain age, there has been little study of the optimal ways of computing the delta. In this technical note we describe problems with the most common current approach, and present potential improvements. We evaluate different estimation methods on simulated and real data. We also find the strongest correlations of corrected brain age delta with 5,792 non-imaging variables (non-brain physical measures, life-factor measures, cognitive test scores, etc.), and also with 2,641 multimodal brain imaging-derived phenotypes, with data from 19,000 participants in UK Biobank. It is of interest to study "brain age'', as inferred from brain imaging data. The delta between brain age and actual age is typically computed. We describe problems with the most common current approach. We present potential improvements. We evaluate methods on simulated data and data from UK Biobank.
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Affiliation(s)
- Stephen M Smith
- Wellcome Trust Centre for Integrative Neuroimaging (WIN-FMRIB), University of Oxford, Oxford, UK.
| | - Diego Vidaurre
- Wellcome Trust Centre for Integrative Neuroimaging (WIN-OHBA), University of Oxford, Oxford, UK
| | - Fidel Alfaro-Almagro
- Wellcome Trust Centre for Integrative Neuroimaging (WIN-FMRIB), University of Oxford, Oxford, UK
| | - Thomas E Nichols
- Big Data Institute, University of Oxford, Oxford, UK; Wellcome Trust Centre for Integrative Neuroimaging (WIN-FMRIB), University of Oxford, Oxford, UK
| | - Karla L Miller
- Wellcome Trust Centre for Integrative Neuroimaging (WIN-FMRIB), University of Oxford, Oxford, UK
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25
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Sundaresan V, Griffanti L, Kindalova P, Alfaro-Almagro F, Zamboni G, Rothwell PM, Nichols TE, Jenkinson M. Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference. Neuroimage 2019; 185:434-445. [PMID: 30359730 PMCID: PMC6299259 DOI: 10.1016/j.neuroimage.2018.10.042] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Revised: 09/05/2018] [Accepted: 10/15/2018] [Indexed: 11/17/2022] Open
Abstract
White matter hyperintensities (WMH), also known as white matter lesions, are localised white matter areas that appear hyperintense on MRI scans. WMH commonly occur in the ageing population, and are often associated with several factors such as cognitive disorders, cardiovascular risk factors, cerebrovascular and neurodegenerative diseases. Despite the fact that some links between lesion location and parametric factors such as age have already been established, the relationship between voxel-wise spatial distribution of lesions and these factors is not yet well understood. Hence, it would be of clinical importance to model the distribution of lesions at the population-level and quantitatively analyse the effect of various factors on the lesion distribution model. In this work we compare various methods, including our proposed method, to generate voxel-wise distributions of WMH within a population with respect to various factors. Our proposed Bayesian spline method models the spatio-temporal distribution of WMH with respect to a parametric factor of interest, in this case age, within a population. Our probabilistic model takes as input the lesion segmentation binary maps of subjects belonging to various age groups and provides a population-level parametric lesion probability map as output. We used a spline representation to ensure a degree of smoothness in space and the dimension associated with the parameter, and formulated our model using a Bayesian framework. We tested our algorithm output on simulated data and compared our results with those obtained using various existing methods with different levels of algorithmic and computational complexity. We then compared the better performing methods on a real dataset, consisting of 1000 subjects of the UK Biobank, divided in two groups based on hypertension diagnosis. Finally, we applied our method on a clinical dataset of patients with vascular disease. On simulated dataset, the results from our algorithm showed a mean square error (MSE) value of 7.27×10-5, which was lower than the MSE value reported in the literature, with the advantage of being robust and computationally efficient. In the UK Biobank data, we found that the lesion probabilities are higher for the hypertension group compared to the non-hypertension group and further verified this finding using a statistical t-test. Finally, when applying our method on patients with vascular disease, we observed that the overall probability of lesions is significantly higher in later age groups, which is in line with the current literature.
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Affiliation(s)
- Vaanathi Sundaresan
- Oxford Centre for Functional MRI of Brain (FMRIB), Wellcome Centre for Integrative NeuroImaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Oxford-Nottingham Centre for Doctoral Training in Biomedical Imaging, University of Oxford, UK; Oxford India Centre for Sustainable Development, Somerville College, University of Oxford, UK.
| | - Ludovica Griffanti
- Oxford Centre for Functional MRI of Brain (FMRIB), Wellcome Centre for Integrative NeuroImaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | | | - Fidel Alfaro-Almagro
- Oxford Centre for Functional MRI of Brain (FMRIB), Wellcome Centre for Integrative NeuroImaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Giovanna Zamboni
- Oxford Centre for Functional MRI of Brain (FMRIB), Wellcome Centre for Integrative NeuroImaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Peter M Rothwell
- Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Thomas E Nichols
- Oxford Centre for Functional MRI of Brain (FMRIB), Wellcome Centre for Integrative NeuroImaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Department of Statistics, University of Warwick, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, UK
| | - Mark Jenkinson
- Oxford Centre for Functional MRI of Brain (FMRIB), Wellcome Centre for Integrative NeuroImaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
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26
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Vidaurre D, Abeysuriya R, Becker R, Quinn AJ, Alfaro-Almagro F, Smith SM, Woolrich MW. Discovering dynamic brain networks from big data in rest and task. Neuroimage 2018; 180:646-656. [PMID: 28669905 PMCID: PMC6138951 DOI: 10.1016/j.neuroimage.2017.06.077] [Citation(s) in RCA: 155] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 06/12/2017] [Accepted: 06/28/2017] [Indexed: 11/29/2022] Open
Abstract
Brain activity is a dynamic combination of the responses to sensory inputs and its own spontaneous processing. Consequently, such brain activity is continuously changing whether or not one is focusing on an externally imposed task. Previously, we have introduced an analysis method that allows us, using Hidden Markov Models (HMM), to model task or rest brain activity as a dynamic sequence of distinct brain networks, overcoming many of the limitations posed by sliding window approaches. Here, we present an advance that enables the HMM to handle very large amounts of data, making possible the inference of very reproducible and interpretable dynamic brain networks in a range of different datasets, including task, rest, MEG and fMRI, with potentially thousands of subjects. We anticipate that the generation of large and publicly available datasets from initiatives such as the Human Connectome Project and UK Biobank, in combination with computational methods that can work at this scale, will bring a breakthrough in our understanding of brain function in both health and disease.
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Affiliation(s)
- Diego Vidaurre
- Oxford Centre for Human Brain Activity (OHBA), University of Oxford, UK.
| | - Romesh Abeysuriya
- Oxford Centre for Human Brain Activity (OHBA), University of Oxford, UK
| | - Robert Becker
- Oxford Centre for Human Brain Activity (OHBA), University of Oxford, UK
| | - Andrew J Quinn
- Oxford Centre for Human Brain Activity (OHBA), University of Oxford, UK
| | - Fidel Alfaro-Almagro
- Oxford University Centre for Functional MRI of the Brain (FMRIB), University of Oxford, UK
| | - Stephen M Smith
- Oxford Centre for Human Brain Activity (OHBA), University of Oxford, UK
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity (OHBA), University of Oxford, UK; Oxford University Centre for Functional MRI of the Brain (FMRIB), University of Oxford, UK
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27
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Elliott LT, Sharp K, Alfaro-Almagro F, Shi S, Miller KL, Douaud G, Marchini J, Smith SM. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature 2018; 562:210-216. [PMID: 30305740 PMCID: PMC6786974 DOI: 10.1038/s41586-018-0571-7] [Citation(s) in RCA: 367] [Impact Index Per Article: 61.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Accepted: 09/04/2018] [Indexed: 12/16/2022]
Abstract
The genetic architecture of brain structure and function is largely unknown. To investigate this, we carried out genome-wide association studies of 3,144 functional and structural brain imaging phenotypes from UK Biobank (discovery dataset 8,428 subjects). Here we show that many of these phenotypes are heritable. We identify 148 clusters of associations between single nucleotide polymorphisms and imaging phenotypes that replicate at P < 0.05, when we would expect 21 to replicate by chance. Notable significant, interpretable associations include: iron transport and storage genes, related to magnetic susceptibility of subcortical brain tissue; extracellular matrix and epidermal growth factor genes, associated with white matter micro-structure and lesions; genes that regulate mid-line axon development, associated with organization of the pontine crossing tract; and overall 17 genes involved in development, pathway signalling and plasticity. Our results provide insights into the genetic architecture of the brain that are relevant to neurological and psychiatric disorders, brain development and ageing.
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Affiliation(s)
| | - Kevin Sharp
- Department of Statistics, University of Oxford, Oxford, UK
| | - Fidel Alfaro-Almagro
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Sinan Shi
- Department of Statistics, University of Oxford, Oxford, UK
| | - Karla L Miller
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Gwenaëlle Douaud
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Jonathan Marchini
- Department of Statistics, University of Oxford, Oxford, UK.
- The Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
| | - Stephen M Smith
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK.
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28
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Bastiani M, Cottaar M, Fitzgibbon SP, Suri S, Alfaro-Almagro F, Sotiropoulos SN, Jbabdi S, Andersson JLR. Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction. Neuroimage 2018; 184:801-812. [PMID: 30267859 PMCID: PMC6264528 DOI: 10.1016/j.neuroimage.2018.09.073] [Citation(s) in RCA: 146] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Revised: 09/21/2018] [Accepted: 09/25/2018] [Indexed: 11/24/2022] Open
Abstract
Diffusion MRI data can be affected by hardware and subject-related artefacts that can adversely affect downstream analyses. Therefore, automated quality control (QC) is of great importance, especially in large population studies where visual QC is not practical. In this work, we introduce an automated diffusion MRI QC framework for single subject and group studies. The QC is based on a comprehensive, non-parametric approach for movement and distortion correction: FSL EDDY, which allows us to extract a rich set of QC metrics that are both sensitive and specific to different types of artefacts. Two different tools are presented: QUAD (QUality Assessment for DMRI), for single subject QC and SQUAD (Study-wise QUality Assessment for DMRI), which is designed to enable group QC and facilitate cross-studies harmonisation efforts. Two tools to automatically perform QC of diffusion MRI data. Automated generation of single subject reports for visual inspection and database. Group databases and reports allow to compare subjects within and between studies. Categorical and continuous variables can be used to update the reports.
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Affiliation(s)
- Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK.
| | - Michiel Cottaar
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Sean P Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Sana Suri
- Department of Psychiatry, University of Oxford, UK; Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Human Brain Activity (OHBA), University of Oxford, UK
| | - Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Stamatios N Sotiropoulos
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK; National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Queens Medical Centre, Nottingham, UK
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Jesper L R Andersson
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
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29
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Abstract
Phrenology was a nineteenth century endeavour to link personality traits with scalp morphology, which has been both influential and fiercely criticised, not least because of the assumption that scalp morphology can be informative of underlying brain function. Here we test the idea empirically rather than dismissing it out of hand. Whereas nineteenth century phrenologists had access to coarse measurement tools (digital technology referring then to fingers), we were able to re-examine phrenology using 21st century methods and thousands of subjects drawn from the largest neuroimaging study to date. High-quality structural MRI was used to quantify local scalp curvature. The resulting curvature statistics were compared against lifestyle measures acquired from the same cohort of subjects, being careful to match a subset of lifestyle measures to phrenological ideas of brain organisation, in an effort to evoke the character of Victorian times. The results represent the most rigorous evaluation of phrenological claims to date.
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Affiliation(s)
- O Parker Jones
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK.
| | - F Alfaro-Almagro
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
| | - S Jbabdi
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
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30
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Alfaro-Almagro F, Jenkinson M, Bangerter NK, Andersson JLR, Griffanti L, Douaud G, Sotiropoulos SN, Jbabdi S, Hernandez-Fernandez M, Vallee E, Vidaurre D, Webster M, McCarthy P, Rorden C, Daducci A, Alexander DC, Zhang H, Dragonu I, Matthews PM, Miller KL, Smith SM. Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 2018; 166:400-424. [PMID: 29079522 PMCID: PMC5770339 DOI: 10.1016/j.neuroimage.2017.10.034] [Citation(s) in RCA: 650] [Impact Index Per Article: 108.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2017] [Revised: 10/16/2017] [Accepted: 10/16/2017] [Indexed: 12/22/2022] Open
Abstract
UK Biobank is a large-scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100,000 of the original participants for brain, heart and body MRI, carotid ultrasound and low-dose bone/fat x-ray. The brain imaging component covers 6 modalities (T1, T2 FLAIR, susceptibility weighted MRI, Resting fMRI, Task fMRI and Diffusion MRI). Raw and processed data from the first 10,000 imaged subjects has recently been released for general research access. To help convert this data into useful summary information we have developed an automated processing and QC (Quality Control) pipeline that is available for use by other researchers. In this paper we describe the pipeline in detail, following a brief overview of UK Biobank brain imaging and the acquisition protocol. We also describe several quantitative investigations carried out as part of the development of both the imaging protocol and the processing pipeline.
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Affiliation(s)
- Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK.
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Neal K Bangerter
- Electrical and Computer Engineering, Brigham Young University, UT, USA
| | - Jesper L R Andersson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Ludovica Griffanti
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Gwenaëlle Douaud
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Stamatios N Sotiropoulos
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Moises Hernandez-Fernandez
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Emmanuel Vallee
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Diego Vidaurre
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, UK
| | - Matthew Webster
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Paul McCarthy
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Christopher Rorden
- Department of Psychology and McCausland Center for Brain Imaging, University of South Carolina, SC, USA
| | - Alessandro Daducci
- Computer Science Department, University of Verona, Italy; Radiology Department, University Hospital Center, Switzerland
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK
| | - Hui Zhang
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK
| | | | - Paul M Matthews
- Division of Brain Sciences, Imperial College, London, UK; UK Dementia Research Institute, London, UK
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
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31
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Bijsterbosch J, Harrison S, Duff E, Alfaro-Almagro F, Woolrich M, Smith S. Investigations into within- and between-subject resting-state amplitude variations. Neuroimage 2017; 159:57-69. [PMID: 28712995 PMCID: PMC5678294 DOI: 10.1016/j.neuroimage.2017.07.014] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Revised: 07/06/2017] [Accepted: 07/10/2017] [Indexed: 12/30/2022] Open
Abstract
The amplitudes of spontaneous fluctuations in brain activity may be a significant source of within-subject and between-subject variability, and this variability is likely to be carried through into functional connectivity (FC) estimates (whether directly or indirectly). Therefore, improving our understanding of amplitude fluctuations over the course of a resting state scan and variation in amplitude across individuals is of great relevance to the interpretation of FC findings. We investigate resting state amplitudes in two large-scale studies (HCP and UK Biobank), with the aim of determining between-subject and within-subject variability. Between-subject clustering distinguished between two groups of brain networks whose amplitude variation across subjects were highly correlated with each other, revealing a clear distinction between primary sensory and motor regions ('primary sensory/motor cluster') and cognitive networks. Within subjects, all networks in the primary sensory/motor cluster showed a consistent increase in amplitudes from the start to the end of the scan. In addition to the strong increases in primary sensory/motor amplitude, a large number of changes in FC were found when comparing the two scans acquired on the same day (HCP data). Additive signal change analysis confirmed that all of the observed FC changes could be fully explained by changes in amplitude. Between-subject correlations in UK Biobank data showed a negative correlation between primary sensory/motor amplitude and average sleep duration, suggesting a role of arousal. Our findings additionally reveal complex relationships between amplitude and head motion. These results suggest that network amplitude is a source of significant variability both across subjects, and within subjects on a within-session timescale. Future rfMRI studies may benefit from obtaining arousal-related (self report) measures, and may wish to consider the influence of amplitude changes on measures of (dynamic) functional connectivity.
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Affiliation(s)
- Janine Bijsterbosch
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK.
| | - Samuel Harrison
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Eugene Duff
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Fidel Alfaro-Almagro
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Mark Woolrich
- Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, UK
| | - Stephen Smith
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
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32
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Gorgolewski KJ, Alfaro-Almagro F, Auer T, Bellec P, Capotă M, Chakravarty MM, Churchill NW, Cohen AL, Craddock RC, Devenyi GA, Eklund A, Esteban O, Flandin G, Ghosh SS, Guntupalli JS, Jenkinson M, Keshavan A, Kiar G, Liem F, Raamana PR, Raffelt D, Steele CJ, Quirion PO, Smith RE, Strother SC, Varoquaux G, Wang Y, Yarkoni T, Poldrack RA. BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. PLoS Comput Biol 2017; 13:e1005209. [PMID: 28278228 PMCID: PMC5363996 DOI: 10.1371/journal.pcbi.1005209] [Citation(s) in RCA: 156] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Revised: 03/23/2017] [Accepted: 02/23/2017] [Indexed: 12/13/2022] Open
Abstract
The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.
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Affiliation(s)
| | - Fidel Alfaro-Almagro
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Oxford University, Oxford, United Kingdom
| | - Tibor Auer
- Department of Psychology, Royal Holloway University of London, Egham, United Kingdom
| | - Pierre Bellec
- Centre de Recherche de l’Institut Universitaire Gériatrique de Montréal, Montreal, Canada
- Department of computer science and operations research, Université de Montréal, Montreal, Canada
| | - Mihai Capotă
- Parallel Computing Lab, Intel Corporation, Santa Clara, CA & Hillsboro, Oregon, United States of America
| | - M. Mallar Chakravarty
- Douglas Mental Health University Institute, McGill University, Montreal, Canada
- Department of Psychiatry McGill University, Montreal, Canada
| | - Nathan W. Churchill
- Keenan Research Centre of the Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Ontario, Canada
| | - Alexander Li Cohen
- Department of Neurology, Boston Children’s Hospital, Boston, Massachusetts, United States of America
| | - R. Cameron Craddock
- Computational Neuroimaging Lab, Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
- Center for the Developing Brain, Child Mind Institute, New York, New York, United States of America
| | - Gabriel A. Devenyi
- Douglas Mental Health University Institute, McGill University, Montreal, Canada
- Department of Psychiatry McGill University, Montreal, Canada
| | - Anders Eklund
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Department of Computer and Information Science, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Oscar Esteban
- Department of Psychology, Stanford University, Stanford, California, United States of America
| | | | - Satrajit S. Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Otolaryngology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - J. Swaroop Guntupalli
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire, United States of America
| | - Mark Jenkinson
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Oxford University, Oxford, United Kingdom
| | - Anisha Keshavan
- UC Berkeley-UCSF Graduate Program in Bioengineering, San Francisco, California, United States of America
| | - Gregory Kiar
- Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Franziskus Liem
- University Research Priority Program "Dynamics of Healthy Aging", University of Zurich, Zurich, Switzerland
| | - Pradeep Reddy Raamana
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - David Raffelt
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
| | - Christopher J. Steele
- Douglas Mental Health University Institute, McGill University, Montreal, Canada
- Department of Psychiatry McGill University, Montreal, Canada
| | - Pierre-Olivier Quirion
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Robert E. Smith
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
| | - Stephen C. Strother
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Gaël Varoquaux
- Parietal team, INRIA Saclay Ile-de-France, Palaiseau, France
| | - Yida Wang
- Parallel Computing Lab, Intel Corporation, Santa Clara, CA & Hillsboro, Oregon, United States of America
| | - Tal Yarkoni
- Department of Psychology, University of Texas at Austin, Austin, Texas, United States of America
| | - Russell A. Poldrack
- Department of Psychology, Stanford University, Stanford, California, United States of America
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33
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Miller KL, Alfaro-Almagro F, Bangerter NK, Thomas DL, Yacoub E, Xu J, Bartsch AJ, Jbabdi S, Sotiropoulos SN, Andersson JLR, Griffanti L, Douaud G, Okell TW, Weale P, Dragonu I, Garratt S, Hudson S, Collins R, Jenkinson M, Matthews PM, Smith SM. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci 2016; 19:1523-1536. [PMID: 27643430 PMCID: PMC5086094 DOI: 10.1038/nn.4393] [Citation(s) in RCA: 936] [Impact Index Per Article: 117.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Accepted: 08/25/2016] [Indexed: 01/17/2023]
Abstract
Medical imaging has enormous potential for early disease prediction, but is impeded by the difficulty and expense of acquiring data sets before symptom onset. UK Biobank aims to address this problem directly by acquiring high-quality, consistently acquired imaging data from 100,000 predominantly healthy participants, with health outcomes being tracked over the coming decades. The brain imaging includes structural, diffusion and functional modalities. Along with body and cardiac imaging, genetics, lifestyle measures, biological phenotyping and health records, this imaging is expected to enable discovery of imaging markers of a broad range of diseases at their earliest stages, as well as provide unique insight into disease mechanisms. We describe UK Biobank brain imaging and present results derived from the first 5,000 participants' data release. Although this covers just 5% of the ultimate cohort, it has already yielded a rich range of associations between brain imaging and other measures collected by UK Biobank.
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Affiliation(s)
- Karla L Miller
- Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
- Corresponding author:
| | - Fidel Alfaro-Almagro
- Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - Neal K Bangerter
- Department of Electrical Engineering, Brigham Young University, Provo, USA
| | - David L Thomas
- Institute of Neurology, University College London, London, UK
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, USA
| | - Junqian Xu
- Icahn School of Medicine at Mount Sinai, New York, USA
| | | | - Saad Jbabdi
- Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
| | | | - Jesper LR Andersson
- Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - Ludovica Griffanti
- Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - Gwenaëlle Douaud
- Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - Thomas W Okell
- Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
| | | | | | | | | | - Rory Collins
- UK Biobank, Stockport, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Mark Jenkinson
- Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - Paul M Matthews
- Division of Brain Sciences, Department of Medicine, Imperial College London, London, UK
| | - Stephen M Smith
- Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
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34
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Janssen J, Alemán-Gómez Y, Schnack H, Balaban E, Pina-Camacho L, Alfaro-Almagro F, Castro-Fornieles J, Otero S, Baeza I, Moreno D, Bargalló N, Parellada M, Arango C, Desco M. Cortical morphology of adolescents with bipolar disorder and with schizophrenia. Schizophr Res 2014; 158:91-9. [PMID: 25085384 DOI: 10.1016/j.schres.2014.06.040] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2014] [Revised: 05/12/2014] [Accepted: 06/24/2014] [Indexed: 01/23/2023]
Abstract
INTRODUCTION Recent evidence points to overlapping decreases in cortical thickness and gyrification in the frontal lobe of patients with adult-onset schizophrenia and bipolar disorder with psychotic symptoms, but it is not clear if these findings generalize to patients with a disease onset during adolescence and what may be the mechanisms underlying a decrease in gyrification. METHOD This study analyzed cortical morphology using surface-based morphometry in 92 subjects (age range 11-18 years, 52 healthy controls and 40 adolescents with early-onset first-episode psychosis diagnosed with schizophrenia (n=20) or bipolar disorder with psychotic symptoms (n=20) based on a two year clinical follow up). Average lobar cortical thickness, surface area, gyrification index (GI) and sulcal width were compared between groups, and the relationship between the GI and sulcal width was assessed in the patient group. RESULTS Both patients groups showed decreased cortical thickness and increased sulcal width in the frontal cortex when compared to healthy controls. The schizophrenia subgroup also had increased sulcal width in all other lobes. In the frontal cortex of the combined patient group sulcal width was negatively correlated (r=-0.58, p<0.001) with the GI. CONCLUSIONS In adolescents with schizophrenia and bipolar disorder with psychotic symptoms there is cortical thinning, decreased GI and increased sulcal width of the frontal cortex present at the time of the first psychotic episode. Decreased frontal GI is associated with the widening of the frontal sulci which may reduce sulcal surface area. These results suggest that abnormal growth (or more pronounced shrinkage during adolescence) of the frontal cortex represents a shared endophenotype for psychosis.
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Affiliation(s)
- Joost Janssen
- Instituto de Investigación Sanitaria Gregorio Marañón, Dr. Esquerdo, 46, 28007 Madrid, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Dr. Esquerdo, 46, 28007 Madrid, Spain; Child and Adolescent Psychiatry Department, Hospital General Universitario Gregorio Marañón, Dr. Esquerdo, 46, 28007 Madrid, Spain; Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.
| | - Yasser Alemán-Gómez
- Instituto de Investigación Sanitaria Gregorio Marañón, Dr. Esquerdo, 46, 28007 Madrid, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Dr. Esquerdo, 46, 28007 Madrid, Spain; Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Avda. de la Universidad, 30, 28911 Leganés, Madrid, Spain
| | - Hugo Schnack
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Evan Balaban
- Behavioral Neurosciences Program, McGill University, N8-15 Stewart Biological Sciences Building, 1205 Docteur Penfield Avenue, Montreal QC H3A 1B1, Canada
| | - Laura Pina-Camacho
- Instituto de Investigación Sanitaria Gregorio Marañón, Dr. Esquerdo, 46, 28007 Madrid, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Dr. Esquerdo, 46, 28007 Madrid, Spain; Department of Child and Adolescent Psychiatry, Institute of Psychiatry, King's College London, 16 de Crespigny Park, London SE5 8AF, UK
| | - Fidel Alfaro-Almagro
- Instituto de Investigación Sanitaria Gregorio Marañón, Dr. Esquerdo, 46, 28007 Madrid, Spain
| | - Josefina Castro-Fornieles
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Dr. Esquerdo, 46, 28007 Madrid, Spain; Department of Child and Adolescent Psychiatry and Psychology, Institut Clinic of Neurosciences, Hospital Clínic Universitari of Barcelona, Villarroel, 170, Barcelona 08036, Spain; Department of Psychiatry and Clinical Psychobiology, University of Barcelona, Casanovas, 143, Barcelona 08036, Spain
| | - Soraya Otero
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Dr. Esquerdo, 46, 28007 Madrid, Spain; Child and Adolescent Mental Health Unit, Department of Psychiatry and Psychology, Hospital Universitario Marqués de Valdecilla, Avda. Valdecilla nº 25, 39008 Santander, Spain
| | - Inmaculada Baeza
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Dr. Esquerdo, 46, 28007 Madrid, Spain; Department of Child and Adolescent Psychiatry and Psychology, Institut Clinic of Neurosciences, Hospital Clínic Universitari of Barcelona, Villarroel, 170, Barcelona 08036, Spain
| | - Dolores Moreno
- Instituto de Investigación Sanitaria Gregorio Marañón, Dr. Esquerdo, 46, 28007 Madrid, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Dr. Esquerdo, 46, 28007 Madrid, Spain; Child and Adolescent Psychiatry Department, Hospital General Universitario Gregorio Marañón, Dr. Esquerdo, 46, 28007 Madrid, Spain
| | - Nuria Bargalló
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Dr. Esquerdo, 46, 28007 Madrid, Spain; Magnetic Resonance Image Core Facility, IDIBAPS (Institut d'Investigacions Biomèdiques August Pi i Sunyer), Barcelona, Spain; Image Diagnostic Center, Hospital Clínic, Barcelona, Spain
| | - Mara Parellada
- Instituto de Investigación Sanitaria Gregorio Marañón, Dr. Esquerdo, 46, 28007 Madrid, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Dr. Esquerdo, 46, 28007 Madrid, Spain; Child and Adolescent Psychiatry Department, Hospital General Universitario Gregorio Marañón, Dr. Esquerdo, 46, 28007 Madrid, Spain
| | - Celso Arango
- Instituto de Investigación Sanitaria Gregorio Marañón, Dr. Esquerdo, 46, 28007 Madrid, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Dr. Esquerdo, 46, 28007 Madrid, Spain; Child and Adolescent Psychiatry Department, Hospital General Universitario Gregorio Marañón, Dr. Esquerdo, 46, 28007 Madrid, Spain
| | - Manuel Desco
- Instituto de Investigación Sanitaria Gregorio Marañón, Dr. Esquerdo, 46, 28007 Madrid, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Dr. Esquerdo, 46, 28007 Madrid, Spain; Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Avda. de la Universidad, 30, 28911 Leganés, Madrid, Spain
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