Study protocol: the Whitehall II imaging sub-study
- Nicola Filippini1, 2,
- Enikő Zsoldos1,
- Rita Haapakoski1,
- Claire E Sexton1,
- Abda Mahmood1,
- Charlotte L Allan1,
- Anya Topiwala1,
- Vyara Valkanova1,
- Eric J Brunner3,
- Martin J Shipley3,
- Edward Auerbach4,
- Steen Moeller4,
- Kâmil Uğurbil4,
- Junqian Xu4, 5,
- Essa Yacoub4,
- Jesper Andersson2,
- Janine Bijsterbosch2,
- Stuart Clare2,
- Ludovica Griffanti2,
- Aaron T Hess6,
- Mark Jenkinson2,
- Karla L Miller2,
- Gholamreza Salimi-Khorshidi2,
- Stamatios N Sotiropoulos2,
- Natalie L Voets2,
- Stephen M Smith2,
- John R Geddes1,
- Archana Singh-Manoux3, 7,
- Clare E Mackay1,
- Mika Kivimäki3 and
- Klaus P Ebmeier1Email author
© Filippini et al.; licensee BioMed Central Ltd. 2014
Received: 16 May 2014
Accepted: 21 May 2014
Published: 30 May 2014
The Whitehall II (WHII) study of British civil servants provides a unique source of longitudinal data to investigate key factors hypothesized to affect brain health and cognitive ageing. This paper introduces the multi-modal magnetic resonance imaging (MRI) protocol and cognitive assessment designed to investigate brain health in a random sample of 800 members of the WHII study.
A total of 6035 civil servants participated in the WHII Phase 11 clinical examination in 2012–2013. A random sample of these participants was included in a sub-study comprising an MRI brain scan, a detailed clinical and cognitive assessment, and collection of blood and buccal mucosal samples for the characterisation of immune function and associated measures. Data collection for this sub-study started in 2012 and will be completed by 2016. The participants, for whom social and health records have been collected since 1985, were between 60–85 years of age at the time the MRI study started. Here, we describe the pre-specified clinical and cognitive assessment protocols, the state-of-the-art MRI sequences and latest pipelines for analyses of this sub-study.
The integration of cutting-edge MRI techniques, clinical and cognitive tests in combination with retrospective data on social, behavioural and biological variables during the preceding 25 years from a well-established longitudinal epidemiological study (WHII cohort) will provide a unique opportunity to examine brain structure and function in relation to age-related diseases and the modifiable and non-modifiable factors affecting resilience against and vulnerability to adverse brain changes.
KeywordsEpidemiology Magnetic resonance imaging Diffusion tensor imaging White matter Functional MRI Connectome Resting state brain networks Neuropsychology Dementia Affective disorders
Over the next few decades, increases in life expectancy will result in fundamental changes to the population structure. Associated with this demographic change, health and social care services will need to cope with a greater prevalence of mental and neurological disorders. Clinical depression and cognitive decline have a combined estimated prevalence of 7-20% in the population over 65 years [1–4]. Furthermore, according to some estimates, the number of people with neurodegenerative disorders will quadruple in the next 20 years causing a significant increase in the cost of care . To extend the productive period in citizens’ lives and to reduce costs of care in late life a greater knowledge of prevention and treatment of these common conditions is needed. This will not be possible without a better understanding of the causal mechanisms of disease and, equally importantly, the factors associated with resilience to age-related dysfunction .
Phases of Whitehall II with available measures
Phase 1 1985-88
Phase 3 91-93
Phase 5 97-99
Phase 7 03-04
Phase 9 07-09
Phase 11 12-13
Social circumstances & behaviour, smoking, alcohol, exercise, sleep diet
Biological measures: blood pressure, BMI, lipids, glucose, insulin, stored blood (−80C)
2-h oral glucose tolerance test
Psychosocial factors: work, social support, − participation, care provision
Health outcomes: CHD, stroke, diabetes, cancer, mortality, medications
Function: physical, social & mental
Cognitive tests, physical/lung function tests
We randomly selected 800 WHII Phase 11 participants to take part in the WHII imaging sub-study, which includes a detailed clinical and cognitive assessment, measurement of immune parameters and a magnetic resonance imaging (MRI) scan. MRI scans provide a non-invasive window into the living brain, giving unique access to understanding normal and pathological processes that affect brain structure and function. Incorporating state-of-the-art imaging techniques and cognitive measures with the WHII’s longitudinal dataset of social, behavioural and biological variables, represents a unique opportunity to study the ageing process and to directly link 25-year exposure history to old-age cognition and a variety of measures of ‘brain health’. Analysis of immunological variables and linkage of these studies with behavioural and imaging data enables a more comprehensive investigation of the pathophysiological processes of dysfunction and cognitive impairment in later life.
In this paper, we provide a description of the study’s organisation and funding structure, its participants’ inclusion/exclusion criteria and of the cognitive, imaging and blood specimen protocols employed in the study. For the imaging protocol, careful consideration has been given to harnessing the most recent technical developments, whilst maintaining clinical relevance. A preliminary description of the techniques to be used to pre-process and examine MRI-related measures will also be presented, and results of a direct comparison between a recently developed and a more standard MRI acquisition approach for investigating brain functional organization will also be shown. We provide an overview of our original hypotheses at the time of application for funding.
Study organization and funding
The sub-study is funded by the Lifelong Health and Wellbeing Phase-3 programme grant “Predicting MRI abnormalities with longitudinal data of the Whitehall II sub-study” (MRC-G1001354; Ebmeier KP (PI), Geddes JR, Kivimäki M, Mackay CE, Singh-Manoux A, Smith SM), as well as the HDH Wills 1965 (English Charity Register: 1117747; Ebmeier KP (PI)), and the Gordon Edward Small Charitable (Scottish Charity Register: SC008962; Ebmeier KP (PI)) Trusts. Collection of blood and buccal mucosal samples for a characterisation of immune function and associated measures is funded by the UK Medical Research Council programme grant K013351 (“Adult determinants of late life depression, cognitive decline and physical functioning - The Whitehall II Ageing Study”, Kivimäki M (PI), Singh-Manoux A, Brunner E, Batty GD, Kumari M, Ebmeier KP, Hingorani A) and the ESRC professional fellowship scheme to Kivimäki.
Ethical approval was granted generically for the “Protocol for non-invasive magnetic resonance investigations in healthy volunteers” (MSD/IDREC/2010/P17.2) by the University of Oxford Central University / Medical Science Division Interdisciplinary Research Ethics Committee (CUREC/MSD-IDREC), who also approved the specific protocol: “Predicting MRI abnormalities with longitudinal data of the Whitehall II sub-study” (MSD-IDREC-C1-2011-71). The Health Research Authority NRES Committee South Central – Oxford B approved the Study: “The Whitehall II Immune Function Sub-study” (REC reference: 13/SC/0072, IRAS project ID: 120516).
The study follows the Medical Research Council (MRC) Policy on data sharing, i.e. images and other data will be available for analysis by other groups after completion of the study, as is the case with the Whitehall II study (see http://www.ucl.ac.uk/whitehallII/data-sharing[8, 9].
Participants’ recruitment and cognitive protocol description
Clinical and cognitive assessment
Each participant recruited for the WHII imaging sub-study undergoes a detailed clinical and cognitive assessment lasting up to two hours.
The clinical assessment consists of a (A) self-administered questionnaire, a (B) semi-structured clinical interview and (C) cognitive assessment (median = 56 minutes, interquartile range: 51–61 minutes).
General Health Questionnaire-30 (GHQ-30;): The GHQ-30 is a 30-item self-administered screening questionnaire for the detection of psychiatric illness that accompanies ill-health, in non-psychiatric clinical and community settings (routinely applied from scan 200).
Mood Disorder Questionnaire (MDQ;): The MDQ is a brief self-report questionnaire for the assessment of life time history of bipolar disorders, based on the DSM-IV.
Centre for Epidemiological Studies Depression Scale (CES-D;): The CES-D is short self-report scale that measures major depressive symptomatology in the general population.
State and Trait Anxiety Inventory (STAI;): The STAI measures both S (state)- and T (trait)-Anxiety in clinical and research settings . It is a self-administered questionnaire that consists of twenty statements assessing how the individual feels at the moment (S-Anxiety) and twenty assessing how they generally feel (administered to n = 15 before routinely applied from scan 200).
CHAMPS Physical Activity Questionnaire for Older Adults: The CHAMPS is a self-administered physical activity questionnaire for older persons. Participants report the weekly frequency and duration of various physical activities, typically undertaken by older adults, allowing calculation of metabolic equivalent of task (MET) and caloric expenditure values per week.
Locus for Causality Exercise Questionnaire (LCE;): The LCE is a 3-item self-administered scale that assesses how much an individual feels that they choose to exercise (internal perceived locus of causality) rather than have to exercise for some reason (external perceived locus of causality). It is thought that individuals are more likely to engage in physical exercise when the perceived locus of causality is internal .
Pittsburgh Sleep Quality Index (PSQI;): The PSQI is a self-rated questionnaire made up of seven component scores that assess sleep quality and disturbance over a one month period in clinical and research settings.
Jenkins Sleep Questionnaire (JSQ;): The JSQ is a 4-item self-rated questionnaire for the assessment of sleep disturbances over a month period.
Life-Orientation Revised (LOT-R;): The LOT-R was devised to measure individual optimism for future events in the general population.
Life Events[21, 22]): A modified version of the List of Threatening Experiences questionnaire (LTE-Q) is used, in which participants are asked about seven types of stressful life events. Participants are asked to remember if any of the events happened to them in the past, and when they happened.
MacArthur stress reactivity questionnaire: It is a nine-item self-rated questionnaire in which the participant is required to rate nine statements on a 5-point scale, regarding to how they handle their emotions in stressful situations.
Penn State Worry Questionnaire Ultra-brief Version (PSWQ;): The PSWQ ultra-brief is the 3-item version of the widely used self-report questionnaire for pathological worry, the 16-item long PSWQ. The 3-items capture pathological worry as defined by the DSM-IV; perceived uncontrollability, multiple domains and high frequency of worry. The PSWQ was introduced into the assessment after scan 200.
Handedness (): It is a self-administered questionnaire that assesses which is the participant’s preferred hand to complete a list of twelve tasks, as well as left-handedness in the family.
Participants also provide information on medical history (detailing hospitalizations, longstanding illnesses, diseases or medical conditions), alcohol and nicotine intake, and general information, such as age and education. Their blood pressure is measured twice in a sitting position, after the cognitive protocol (OMRON HEM-907; OMRON Healthcare UK Ltd., Milton Keynes).
Semi-structured clinical interview
Structured Clinical Interview for DSM-IV-TR Axis I Disorders: The SCID-I is a semi-structured interview for diagnosing current and past DSM-IV Axis I disorders and is administered by a trained graduate psychologist or psychiatrist.
Further structured assessments
Further tests were performed when the clinical history and SCID data of the participant suggested a diagnosis and indicated that a more detailed assessment was required (the number of each test carried out so far is listed in brackets):
Hamilton Depression Scale (HAMD;): The HAMD is a 17-item severity scale administered to individuals diagnosed with ‘affective disorder of depressive type’. It has been devised to quantify the intensity of the depressive symptoms of the patient, based on the necessary information elicited by the interviewer. This scale was administered to n = 16 participants currently symptomatic on the SCID-I .
Young Mania Rating Scale (YMRS;): The YMRS is an 11-item rating scale for the assessment of manic symptoms based on the subjective report of the patient’s experience over the past forty-eight hours. It follows a rating style of symptom severity similar to that of the HAMD  and is administered to participants currently symptomatic on the SCID-I  (n = 2).
Yale-Brown Obsessive Compulsive Scale (Y-BOCS;[29, 30]): The Y-BOCS is a clinician-rated ten-item scale of the severity of symptoms of obsessive-compulsive disorder, with separate subtotals for obsessions and compulsions. This scale is applied to participants currently symptomatic on the SCID-I  (n = 0).
CAGE Questionnaire: Four questions make up this questionnaire to detect dependence on alcohol. They request information on whether the individual needs to “Cut down” their drinking, feels Annoyed by criticism of their drinking, feels Guilty about their alcohol use, and whether they use alcohol first thing in the morning as an ‘Eye-opener’. This scale is administered to participants currently symptomatic on the SCID-I  (n = 6).
Brief Psychiatric Rating Scale (BPRS;): The BPRS is an 18-24-point rating scale for the assessment of psychotic symptoms, and is used in both clinical and research settings. This scale is administered to participants currently symptomatic on the SCID-I  (n = 0).
Cognitive test battery administered to all participants:
Montreal Cognitive Assessment (MoCA;): The MoCA is a 30-point cognitive screening test assessing multiple cognitive domains: a) visuo-spatial abilities (4 points), assessed using a three-dimensional cube-drawing (1 point) and a clock-drawing task (3 points); b) short-term memory recall task (5 points), which involves learning 5 nouns and recalling them approximately 5 minutes afterwards; c) executive function (3 points), which include an alternation task (1 point) and a verbal abstraction task (2 points); d) attention, orientation and working memory (6 points), which are evaluated using a forward- and backward-digit task (2 points), a sustained attention task (1 point), and a serial subtraction task (3 points); e) language (6 points), which is measured using a three-item naming task (3 points), the repetition of two syntactically complex sentences (2 points) and a phonemic fluency task (1 point); and f) orientation to time and space (6 points). Participants receive an additional (1 point) if their education level is ≤ 12 years. Since the MoCA assesses multiple cognitive domains, it is a useful cognitive screening tool for several neurological diseases, such as Parkinson’s disease, vascular cognitive impairment, Huntington’s disease, multiple sclerosis, and other conditions, such as traumatic brain injury, depression and schizophrenia [33, 34].
Trail Making Test (TMT) versions A and B ([35, 36]: The TMT is a visual attention and task-switching test consisting of two parts in which the subject is instructed to connect a set of twenty-five consecutive dots (A: numbers and B: numbers and letters) on a sheet of paper as fast as possible while still maintaining accuracy. It provides information about visual search speed, speed of processing, mental flexibility, as well as executive functioning . It is sensitive to the detection of cognitive impairment including Alzheimer’s disease .
Rey Complex Figure Test and Recognition Trial (RCFT;[38, 39]): The RCF involves copying and then recalling a complex geometric diagram at increasing time intervals . Different cognitive abilities are needed for a correct performance, including visuo-spatial abilities, memory, attention, planning, and working memory. It is used to investigate the effects of brain injury and to test the presence of neurodegenerative conditions .
Verbal fluency test (adapted from the Addenbrooke’s Cognitive Examination Revised (ACE-III)): The verbal fluency test requires participants to say as many words as possible from a category (animals) in a specified time (60 seconds). It is used to investigate the presence of cognitive impairment, neurodegenerative and psychiatric disorders .
Hopkins Verbal Learning Test-Revised (HVLT-R;): The HVLT-R test provides a measure of verbal learning and memory ability [45, 46]. The participant is required to learn a list of twelve words over the course of three trials, and recall and recognise them at increasing time intervals. It is widely used to test the presence of amnestic disorders [47, 48].
Boston Naming Test (BNT-60;): The BNT-60 is a 60-item test graded in difficulty used to measure semantic memory ability and requires naming of a series of images shown to the participant . It is used in individuals with aphasia or any language disturbance caused by neurological insults, such as stroke or neurodegenerative disorders .
Digit Span (DS) and Coding (DC) tests from the Wechsler Adult Intelligence Scale - Fourth Edition (WAIS-IV;): The DS test is used to investigate short-term memory abilities. It includes recall of a lengthening list of digits forwards, backwards, and rearranged in ascending sequence (DSF, DSB, DSS) [53, 54]. In the DC test participants have to write the appropriate novel symbol for each number within a given time.
Test of Premorbid Functioning (TOPF;): The TOPF consists of a list of seventy written words, which must be read aloud and is marked according to pronunciation. The TOPF is used to estimate an individual's level of intellectual functioning before the onset of injury or illness. Premorbid IQ can be calculated from the raw score, adjusted for sex and years of education.
Dots and letters (adapted from the Addenbrooke’s Cognitive Examination III;): The participant is asked to count four sets of dots without pointing to them and identify four partially drawn letters. These tasks assess perceptual abilities.
CLOX: The CLOX is a clock drawing task; in the first part the participant is given a set of instructions to draw a clock and in the second part the examiner draws a clock face, which the participant then has to copy. The CLOX was designed to assess executive impairment and non-executive failure, and is used to discriminate dementia sub-groups .
Cambridge Neuropsychological Test Automated Battery Reaction Time touchscreen version (CANTAB RTI; CANTABeclipse 5.0; Cambridge Cognition Ltd. http://www.camcog.com ): The CANTAB RTI is a computerised (touchscreen) latency task that measures latency and movement time without having to control for tremor. The task is divided into a simple and 5-choice reaction time stage. During the task the participant must react as soon as a yellow dot appears; moving their finger on the screen from a pre-defined location to the location of the yellow dot. In the simple stage the yellow dot always appears in the same location, and in the five-choice stage in one out of five potential locations. The CANTAB RTI is often used to assess visuo-spatial and visuo-motor coordination abilities , motor speed , and understand sustained attention and reaction time .
Purdue Pegboard Test[60, 61]: The Purdue Pegboard measures two types of dexterity; gross movement of the fingers, hands and arms, and fine fingertip dexterity. The participant places pins into a row of holes using right, left and both hands (gross movement) and assembles a set of structures from pins, collars and washers using both hands (fine dexterity) as fast as they can, within a given time. The Purdue Pegboard test was devised for employee selection for industrial jobs but is also used in clinical settings. Impaired peg placement was found among patients with Parkinson’s disease [62, 63]), Huntington’s disease  and schizophrenia . The Purdue Pegboard can also aid lateralization of function . Healthy older people without neuropsychiatric or other disease who showed MRI white matter hyperintensities (WMH) performed worse on the assembly subtest (fine dexterity) than those without WMH .
Imaging protocol description
MRI sequences and parameters used in the study; S and T define Sagittal and Transversal orientation, respectively
TR in ms
TE in ms
Voxel in mm 3
TI in ms
N. of volumes
N. of directions
60 + 5 b = 0 s
6 m 12 s
4 m 14 s
4 m 17 s
9 m 56 s
10 m 10 s
This sequence is primarily used to study grey matter (GM) structural macroscopic tissue in both cortical and subcortical brain regions. GM reductions have been widely associated with impending disease and age-related cognitive dysfunction [67–69].
A Multi-Echo MPRAGE (MEMPR) with motion correction, developed at the Massachusetts General Hospital (MGH, Boston), was employed [70, 71]. This sequence has the advantage of combining the properties of the classical MPRAGE sequence, which has high contrast aiding cortical segmentation, with Multi-Echo FLASH, which improves segmentation of subcortical regions.
Diffusion MRI (dMRI)
Diffusion MRI exploits the principles of traditional MRI to measure the random motion of water molecules and subsequently to 1) infer information about white matter (WM) microstructural properties and 2) delineate the gross axonal organisation of the brain . WM is characterised by bundles of myelinated axons surrounded by myelin sheaths that are built up by layers of membranes. This restricts diffusion of free water molecules; i.e. the myelin layers and the axonal membrane cause a lower restriction along than across the axon and thus a higher anisotropy.
A number of strategies were used to minimise distortions caused by, for example, magnetic susceptibility, eddy-currents, and subject-motion. We employed monopolar diffusion encoding gradients with parallel imaging (GRAPPA) to minimise echo time, which increases the signal to noise ratio (SNR), at the cost of a small increase in eddy-current distortion. We used a recently developed dMRI correction strategy that takes advantage of the complementary information from pairs of diffusion images acquired with reversed phase-encoding (PE) directions to correct for susceptibility-induced distortions . A single non-diffusion weighted (b-value = 0 s/mm2) volume with reversed PE was combined with the non-reversed dMRI data to estimate an off-resonance field, which is then applied to correct susceptibility distortions .
Resting-state functional MRI (rfMRI)
rfMRI is used to investigate resting state networks (RSNs), which comprise brain regions sharing a common time-course of spontaneous fluctuations, and are thought to represent functionally-critical neuronal networks that reflect properties of functional brain organisation . RSNs have been consistently observed across subjects, sessions and functional brain imaging modalities (fMRI, PET, EEG), and their presence has also been reported in studies when participants were asleep, and in anaesthetized monkeys and rats [79–85]. Although potentially harder to interpret than task-based fMRI, the rfMRI approach has the considerable advantage of providing an assay of brain function without requiring subjects to undertake a specific task, particularly in cases where a subject may be less able to understand and/or respond to complex instructions.
We compared a recently developed Multiband MRI sequence [86, 87] with ‘standard’ EPI. Multiband provides a considerable improvement in temporal (Multiband: 1.3 seconds vs. Standard EPI: 3 seconds) and spatial (Multiband: 2 mm isotropic vs. Standard EPI: 3 mm isotropic) resolution, which allows: a) better definition of the spatial maps, b) wider frequency range exploration in time-series analyses and c) more detailed network analyses. To ensure that the new multiband sequence was robust in our older population, we acquired both sequences (standard and multiband) on a subset of participants (N = 76). Results of this comparison will be presented in the next section. In all cases subjects were instructed to lie in dimmed light with their eyes open, blink normally, but not to fall asleep.
rfMRI data pre-processing (motion correction, brain extraction, high-pass temporal filtering with a cut-off of 100 s, and field-map correction is carried out using MELODIC (Multivariate Exploratory Linear Optimized Decomposition into Independent Components, part of FSL http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/melodic/) [88, 89]. In order to reduce the presence of spatially and/or temporally-related artefacts a data-cleaning approach is applied. Single-subject independent component analysis (ICA) is followed by an automatic component classification with FMRIB's ICA-based X-noiseifier (FIX) to identify and regress out the “signal” of the artefactual components reflecting non-neuronal fluctuations [90, 91]. The pre-processed and “cleaned” functional data are registered to the individual's structural scan and standard space images using FNIRT, then optimized using boundary-based-registration approach , and finally spatially smoothed using an isotropic Gaussian kernel of 6 mm full width at half maximum (FWHM).
Fluid attenuated inversion recovery (FLAIR)
T2* (T2 star)
The T2* sequence allows the identification of cerebral microbleeds, which reflect small deposits of the iron-storing protein hemosiderin in the brain and may be a sign of cerebral small-vessel disease [101, 102]. Microbleeds can be found all over the brain and have been shown to be associated with neurological dysfunction and both clinical and cognitive impairment [103, 104]. Their underlying mechanism is still under investigation but the deleterious effect, probably due to inflammatory effects, has been proved to affect neuron functionality and/or cortical cerebral activity . All axial slices of T2* images are visually explored performed by trained neuroscientists and microbleeds identified (Figure 6C).
Blood specimen protocol for the characterization of immune function
Following the MRI examination, blood samples (3 × 8 ml) and two buccal mucosal epithelial samples are taken from each participant. Blood samples are drawn using Vacutainer CPT tubes (Becton Dickinson) in a single venepuncture, inverted 8 times to mix anticoagulant additive with blood and processed within two hours of collection. Buccal mucosal epithelial cells are collected by nylon swabs (microbiome sample) and by twirling the brush against the epithelium and shaking the brush in RNAlater solution for 15 sec to remove the cells (RNA sample). CPT tubes are centrifuged in 1600 g for 30 min, allowing the separation of serum from white blood cells and from red blood cells. After the centrifugation, serum samples and peripheral blood mononuclear cells (PBMCs), i.e. T cells, B cells and NK cells are collected. PBMC cells are washed with phosphate-buffered saline (PBS), counted under microscope and cryopreserved using Cell Freezing Medium (5% DMSO/11%HSA in PBS). All samples are stored in −80°C for later analysis.
In order to investigate immune function in sample material, PBMC cells are stimulated in vitro with αCD3/αCD28 mAb and with TLR2/1 ligand Pam3CSK4 (Pam3Cys) and TLR4 ligand LPS in RPMI at 37°C in 5% CO2. Cells and supernatants are collected after 6 and 24 h of stimulation and stored for later analysis. RNA samples are investigated using RT-PCR or transcriptomics, while serum samples and cell supernatants are analysed with ELISA, Luminex or proteomics. Also, genomics/epigenomics and metabolomics techniques as well as advanced bioinformatics tools will be employed in the sample analysis.
We will examine the following overarching hypotheses in the sub-cohort recruited from WH II Phase 11:
(1) Early risk factors for cognitive dysfunction and late onset depression (such as cardio-vascular risk, see (i.) to (v.) below) will be associated with cerebral atrophy, reduced perfusion and impaired white matter integrity. Resulting impaired cognitive function (as measured by tests of episodic memory and executive function, see below) will be associated with specific brain volumes measured by advanced methods such as voxel based analysis and the automatic identification of specific structures, such as hippocampus, and also with reduced white matter integrity, quantified both globally , and homing in on fibres of interest (including measures of structural connectivity) ;
(2) Resilience, measured in the sub-study directly  and by the absence of depression and cognitive impairment particularly in the presence of higher early risk (see above), is associated with high white matter integrity , and increased coherence of frontal regions in resting state networks (which provides a measure of functional connectivity)  or (compensatory) increased frontal BOLD signal in a memory encoding task . The most important risk factors will be: (i) Antecedent vascular and metabolic risk trajectories and morbidity (adverse major blood lipids/apolipoproteins, hyperglycaemia and diabetes, adiposity, high blood pressure, smoking, chronic inflammation (C-reactive protein, interleukin 6), ECG abnormalities, angina, myocardial infarction, stroke; Framingham cardiovascular risk scores); (ii) low antecedent levels of physical and mental activity (measured by questionnaire [110–112]); (iii) baseline cognitive performance levels and up to 15-year gradients of memory and executive function decrement; (iv) history of depressed mood (3 to 8 repeat measurements using the CESD  and the GHQ , (v) genotype (APOE4 plus >48 k single nucleotide polymorphisms relevant to cardiovascular, metabolic and inflammatory syndromes ). The primary clinical hypotheses which we will address are: a) The quality of frontal compensatory activity will be affected by vascular risk, hypertension, including absence of protective factors, such as physical fitness, cardio-vascular and cerebrovascular prophylaxis (aspirin, statins, antihypertensives), by mental activity (“use it or lose it”), and by frontal lobe atrophy (as observed in treatment resistant depression); b) Time-trajectories derived from the WHII data set, e.g. of cognitive function or depression scores, and vascular risks-factor trajectories, will account for more of the diversity of outcome than singular measurements during any one of the previous follow-phases. This may allow imputations about the natural history of brain changes, but from clinician’s point of view, the least labour and time-intensive predictive test will be the most attractive. An important clinical task would, therefore, be to determine the minimum data set to predict outcome. c) Overall resilience, as supported by the absence of current and past affective or cognitive symptoms and good performance , can be modelled longitudinally from observations antecedent to medial temporal atrophy and frontal compensatory activity and structural integrity (high FA in DTI scan) as described in (a) and (b). In addition, we will address the following specific hypotheses which are of independent interest but will also support the primary findings: (d) Global cognitive performance depends on both hippocampal and frontal integrity/connectivity and (compensatory) frontal activity measured relatively higher frontal coherence within executive resting state networks; (e) Clinical impairment (outcome) appears if functional frontal compensation does not keep up with the degree of hippocampal and frontal atrophy observed; (f) First onset of depression after the age of 65 compared with no depression ever (outcome), matched for age, sex, education, and potential causal factors, such as vascular risk, is associated with reduced frontal/executive network structural and functional connectivity; (g) Clinical impairment may present as impaired cognition and/or as major depressive syndrome depending on the frontal networks affected; (h) Greater hippocampal atrophy will be observed with increasing age and APOE ϵ4 alleles; the absence of antidepressant medication; and among those with a family history of dementia.
The proposed programme of work will lead to the development and validation of methods and measures to integrate biological, physical, psychological, and socio-economic markers or indicators of health and wellbeing in later life, combining the Whitehall II repeat data from 1985 onwards and the psychiatric assessment in Phase 11 (2012–13), with detailed structural and functional imaging data collected in 2012–2016.
Our focus will be on understanding the development and impact of age-related conditions such as depression and cognitive decline, their implications for employment and work in later life, and specifically the neural mechanisms of compensation for cognitive decline and resilience to age-related stress by identifying the mechanisms of neural scaffolding, and the factors and mechanisms associated with successful ageing in the face of brain changes and risk factors. This hopefully will generate ways of protecting against age-related cognitive decline - an approach that is urgently needed, given the current limited progress in specifically preventing dementia.
The programme requires the close collaboration between the epidemiological and clinical team at UCL, the psychiatric and neuropsychological teams in the Oxford Department of Psychiatry and the expertise and resources of the neuroimaging centre at FMRIB. The research programme not only crosses boundaries within the MRC, but also involves engineering and physics components by virtue of its complex MRI acquisition and analysis protocol. The Whitehall II data-base includes extensive socio-economic data, which will allow testing of relevant hypotheses that cross over to risk factors employed in medical epidemiology, and cover the entire adult life course from early adulthood (age 35) to old age. The programme is relevant to biological mechanisms within the neurosciences, as the imaging methods employed will require further interpretation and possible adjunct projects to investigate the biological mechanism responsible for MRI abnormalities. There are of course other large-scale projects, including the UK Biobank and the Connectome Project. One aspect of specific added value in the Whitehall MRI substudy will be the availability of a fine-grained chronological clinical and life-style record with very detailed cognitive assessment and comprehensive MRI data covering structural and functional brain connectivity.
- 3 T:
3 Tesla (magnetic field strength)
Addenbrooke’s cognitive examination 3rd revision
Apolipoprotein E gene/allele
- B cells:
B lymphocytes play a role in the humoral immunity of the adaptive immune system
Becton Dickinson (company)
Boston naming (60-item) test
MRI blood oxygen level dependent
Brief psychiatric rating scale
- CANTAB RTI:
Cambridge neuropsychological test automated battery reaction time
Centre for epidemiological studies depression scale
Community Healthy Activities Model Program for Seniors (Physical Activity Questionnaire for Older Adults)
Coronary heart disease
Cell preparation tube
Central University Research Ethics Committee
Digit coding test
Digit span test
Digit span backwards
Digit span forwards
Diagnostic and Statistical Manual 4th edition
Digit span rearranged in ascending sequence
MRI diffusion tensor imaging
Diffusion weighted MRI
Enzyme-linked immunosorbent assay
MRI echo planar imaging
FMRIB’s automated segmentation tool
FMRIB’s model-based segmentation/registration tool
FMRIB’s ICA-based X-noiseifier
MRI fluid attenuated inversion recovery
MRI fast low angle shot
FMRIB’s linear image registration tool
- FMRIB Centre:
Functional Magnetic Resonance Imaging of the Brain Centre (Oxford)
FMRIB’s nonlinear image registration tool
FMRIB software library
Full width at half maximum (spatial resolution)
General health questionnaire-30
Brain grey matter
Hamilton depression scale
Human connectome project
Heart rate variability
Human serum albumin
Hopkins verbal learning test-revised
Independent component analysis
Jenkins sleep questionnaire
Locus for causality exercise questionnaire
List of threatening experiences questionnaire
Mood disorder questionnaire
Massachusetts general hospital
Montreal cognitive assessment
MRI magnetization-prepared 180 degrees radio-frequency pulses and rapid gradient-echo
Medical Research Council (UK)
Magnetic resonance imaging
Medical Science Division Interdisciplinary Research Ethics Committee
- NK cells:
Natural killer cells (or NK cells) are a type of cytotoxic lymphocyte critical to the innate immune system
Oxford Centre for Clinical Magnetic Resonance
(S)-(2,3-bis(palmitoyloxy)-(2RS)-propyl)-N-palmitoyl-(R)-Cys-(S)-Ser(S)-Lys4-OH trihydrochloride (synthetic lipopeptide, TLR2/1 agonist)
Peripheral blood mononuclear cells
Phosphate buffered saline
Positron emission tomography
Pittsburgh sleep quality index
Penn state worry questionnaire ultra-brief version
- Q-Q plots:
Rey complex figure test
Research Ethics Committee
Roswell Park Memorial Institute
Resting-state functional MRI
Resting State Network
Reverse transcription polymerase chain reaction
Structured clinical interview for DSM-IV-TR axis I disorders
Signal to noise ratio
State and trait anxiety inventory
- T cells:
T lymphocytes play a central role in cell-mediated immunity
MRI spin–lattice relaxation time
MRI measure of the loss of coherence in an ensemble of spins that includes all interactions (including static dephasing)
Trail making test
Test of premorbid functioning
University College London
Wechsler Adult Intelligence Scale - fourth edition
The Whitehall II study
- WHII Phase 11:
Whitehall Two Phase 11 (2011–2012)
Brain white matter
Yale-Brown obsessive compulsive scale
Young mania rating scale.
Thank you to all participants and to the Whitehall staff at UCL, who so helpfully collaborated with us. We are grateful to all FMRIB staff, in particular radiographers Jon Campbell, Michael Sanders, Caroline Young and David Parker, IT staff David Flitney, Christopher Gallagher, Duncan Mortimer and Matthew Webster, FMRIB administrative staff Sue Field and Marilyn Goulding, and last but not least my PA Amanda Pipkin, for help with coordinating the appointments. We would like to thank Martin Turner and his colleagues for advising on incidental findings and taking over clinical responsibility for such participants. We are grateful for provision of the multiband pulse sequence and reconstruction algorithms to the Center for Magnetic Resonance Research, University of Minnesota, USA. We are grateful to Siemens Healthcare for the provision of the DTI and MEMPR sequences. The DTI sequence was a Works-in-Progress package for advanced EPI diffusion imaging, developed by Thorsten Feiweier, Siemens AG, Healthcare Sector, Erlangen, Germany. The Multi-Echo MPRAGE sequence is a Works-in-Progress package, developed by Siemens in collaboration with the Athinoula A. Martinos, Center for Biomedical Imaging, Massachusetts General Hospital. Work on this study was mainly funded by the “Lifelong Health and Wellbeing” Programme Grant: “Predicting MRI abnormalities with longitudinal data of the Whitehall II Substudy” (UK Medical Research Council: G1001354). Collection of blood and buccal mucosal samples for a characterisation of immune function and associated measures is supported by the UK Medical Research Council grant K013351 and the ESRC professional fellowship scheme to Kivimäki. NF and AM are funded by the HDH Wills 1965 Charitable Trust (Nr: 1117747). CLA and AT are supported by Oxford University Clinical Academic Graduate School. NLV is funded by the Medical Research Council, RH by the Economic and Social Research Council (ES/J023299/1). KU receives funding from NIH (NIH U54MH091657, NIH P41 EB015894). LG was funded by Ricerca Corrente 2012–2013 (Italian Ministry of Health). KLM is funded by the Wellcome Trust. SNS is funded by the WU-Minn Human Connectome Project (1U54MH091657-01) from the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; ASM receives research support from the US National Institutes of Health (R01AG013196, R01AG034454). Some researchers (CEM, NLV) are supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre based at Oxford University Hospitals NHS Trust and University of Oxford (The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health).
- Berr C, Wancata J, Ritchie K: Prevalence of dementia in the elderly in Europe. Eur Neuropsychopharmacol. 2005, 15 (4): 463-471. 10.1016/j.euroneuro.2005.04.003.PubMedGoogle Scholar
- Blazer DG: Depression in late life: review and commentary. J Gerontol A Biol Sci Med Sci. 2003, 58 (3): 249-265. 10.1093/gerona/58.3.M249.PubMedGoogle Scholar
- Huang CQ, Wang ZR, Li YH, Xie YZ, Liu QX: Cognitive function and risk for depression in old age: a meta-analysis of published literature. Int Psychogeriatr. 2011, 23 (4): 516-525. 10.1017/S1041610210000049.PubMedGoogle Scholar
- Steffens DC, Fisher GG, Langa KM, Potter GG, Plassman BL: Prevalence of depression among older Americans: the Aging, Demographics and Memory Study. Int Psychogeriatr. 2009, 21 (5): 879-888. 10.1017/S1041610209990044.PubMedPubMed CentralGoogle Scholar
- Brookmeyer R, Johnson E, Ziegler-Graham K, Arrighi HM: Forecasting the global burden of Alzheimer's disease. Alzheimers Dement. 2007, 3 (3): 186-191. 10.1016/j.jalz.2007.04.381.PubMedGoogle Scholar
- Spijker J, MacInnes J: Population ageing: the timebomb that isn't?. BMJ. 2013, 347: f6598-10.1136/bmj.f6598.PubMedGoogle Scholar
- Marmot M, Brunner E: Cohort Profile: the Whitehall II study. Int J Epidemiol. 2005, 34 (2): 251-256. 10.1093/ije/dyh372.PubMedGoogle Scholar
- Medical Research Council (UK): MRC ethics series Good research practice: − Principles and guidelines. 2012, London: Medical Research Council (UK), URL: http://www.mrc.ac.uk/documents/pdf/good-research-practice-principles-and-guidelines/ Google Scholar
- Medical Research Council (UK): MRC Guidance on data sharing requirements for population and patient studies. 2011, London: Medical Research Council (UK), URL: http://www.mrc.ac.uk/news-events/publications/mrc-policy-and-guidance-on-sharing-of-research-data-from-population-and-patient-studies/ Google Scholar
- Goldberg D, Williams P: A user’s guide to the general health questionnaire. 2006, London: GL Assessment LimitedGoogle Scholar
- Hirschfeld RM: The Mood Disorder Questionnaire: A Simple, Patient-Rated Screening Instrument for Bipolar Disorder. Prim Care Companion J Clin Psychiatry. 2002, 4 (1): 9-11. 10.4088/PCC.v04n0104.PubMedPubMed CentralGoogle Scholar
- Radloff LS: The CES-D Scale: A Self-Report Depression Scale for Research in the General Population. Applied Psychological Measurement. 1977, 1: 385-401. 10.1177/014662167700100306.Google Scholar
- Spielberger CD: State-Trait Anxiety Inventory: A comprehensive bibliography. 1983, Palo Alto, CA: Consulting Psychologists PressGoogle Scholar
- Spielberger CD: Theory and research on anxiety. Anxiety and behavior. Edited by: Spielberger CD. 1966, New York: AcademicGoogle Scholar
- Stewart AL, Mills KM, King AC, Haskell WL, Gillis D, Ritter PL: CHAMPS physical activity questionnaire for older adults: outcomes for interventions. Med Sci Sports Exerc. 2001, 33 (7): 1126-1141.PubMedGoogle Scholar
- Deci EL, Ryan RM: Intrinsic Motivation and Self-Determination in Human Behavior. 1985, New York: PlenumGoogle Scholar
- Markland D: Self-determination moderates the effects of perceived competence on intrinsic motivation in an exercise setting. J Sport Exerc Psychol. 1999, 21 (4): 351-361.Google Scholar
- Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ: The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989, 28 (2): 193-213. 10.1016/0165-1781(89)90047-4.PubMedGoogle Scholar
- Jenkins CD, Stanton BA, Niemcryk SJ, Rose RM: A scale for the estimation of sleep problems in clinical research. J Clin Epidemiol. 1988, 41 (4): 313-321. 10.1016/0895-4356(88)90138-2.PubMedGoogle Scholar
- Scheier MF, Carver CS, Bridges MW: Distinguishing Optimism from Neuroticism (and Trait Anxiety, Self-Mastery, and Self-Esteem) - a Reevaluation of the Life Orientation Test. J Pers Soc Psychol. 1994, 67 (6): 1063-1078.PubMedGoogle Scholar
- Brugha T, Bebbington P, Tennant C, Hurry J: The List of Threatening Experiences: a subset of 12 life event categories with considerable long-term contextual threat. Psychol Med. 1985, 15 (1): 189-194. 10.1017/S003329170002105X.PubMedGoogle Scholar
- Brugha TS, Cragg D: The List of Threatening Experiences: the reliability and validity of a brief life events questionnaire. Acta Psychiatr Scand. 1990, 82 (1): 77-81. 10.1111/j.1600-0447.1990.tb01360.x.PubMedGoogle Scholar
- Taylor SE, Seeman TE: Psychosocial resources and the SES-health relationship. Ann N Y Acad Sci. 1999, 896: 210-225. 10.1111/j.1749-6632.1999.tb08117.x.PubMedGoogle Scholar
- Berle D, Starcevic V, Moses K, Hannan A, Milicevic D, Sammut P: Preliminary Validation of an Ultra-brief Version of the Penn State Worry Questionnaire. Clin Psychol Psychother. 2011, 18 (4): 339-346. 10.1002/cpp.724.PubMedGoogle Scholar
- Briggs GG, Nebes RD: Patterns of hand preference in a student population. Cortex. 1975, 11 (3): 230-238. 10.1016/S0010-9452(75)80005-0.PubMedGoogle Scholar
- First MB, Gibbon M, Spitzer RL, Williams JBW: User's Guide for the Structured Clinical Interview for DSM-IV-TR Axis I Disorders - Research Version - (SCID-I for DSM-IV-TR, November 2002 Revision. 2002, New York: Biometric Research Department, New York State Psychiatric IntituteGoogle Scholar
- Hamilton M: A Rating Scale for Depression. J Neurol Neurosurg Psychiatry. 1960, 23 (1): 56-62. 10.1136/jnnp.23.1.56.PubMedPubMed CentralGoogle Scholar
- Young RC, Biggs JT, Ziegler VE, Meyer DA: A rating scale for mania: reliability, validity and sensitivity. Br J Psychiatry. 1978, 133: 429-435. 10.1192/bjp.133.5.429.PubMedGoogle Scholar
- Goodman WK, Price LH, Rasmussen SA, Mazure C, Fleischmann RL, Hill CL, Heninger GR, Charney DS: The Yale-Brown Obsessive Compulsive Scale: I. Development, use, and reliability. Arch Gen Psychiatry. 1989, 46 (11): 1006-1011. 10.1001/archpsyc.1989.01810110048007.PubMedGoogle Scholar
- Goodman WK, Price LH, Rasmussen SA, Mazure C, Delgado P, Heninger GR, Charney DS: The Yale-Brown Obsessive Compulsive Scale: II. Validity. Arch Gen Psychiatry. 1989, 46 (11): 1012-1016. 10.1001/archpsyc.1989.01810110054008.PubMedGoogle Scholar
- Ewing JA: Detecting alcoholism: The CAGE questionnaire. JAMA. 1984, 252 (14): 1905-1907. 10.1001/jama.1984.03350140051025.PubMedGoogle Scholar
- Overall JE, Gorham DR: The Brief Psychiatric Rating-Scale. Psychol Rep. 1962, 10 (3): 799-812. 10.2466/pr0.19126.96.36.1999.Google Scholar
- Nasreddine ZS, Phillips NA, Bedirian V, Charbonneau S, Whitehead V, Collin I, Cummings JL, Chertkow H: The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005, 53 (4): 695-699. 10.1111/j.1532-5415.2005.53221.x.PubMedGoogle Scholar
- Smith T, Gildeh N, Holmes C: The Montreal Cognitive Assessment: validity and utility in a memory clinic setting. Can J Psychiatry. 2007, 52 (5): 329-332.PubMedGoogle Scholar
- Lezak MD, Howieson DB, Loring DW: Neuropsychological Assessment. 2004, New York: Oxford University Press, 4Google Scholar
- Gaudino EA, Geisler MW, Squires NK: Construct validity in the Trail Making Test: what makes Part B harder?. J Clin Exp Neuropsychol. 1995, 17 (4): 529-535. 10.1080/01688639508405143.PubMedGoogle Scholar
- Tombaugh TN: Trail Making Test A and B: normative data stratified by age and education. Arch Clin Neuropsychol. 2004, 19 (2): 203-214. 10.1016/S0887-6177(03)00039-8.PubMedGoogle Scholar
- Meyers JE, Meyers KR: Rey complex figure test and recognition trial: Professional manual. 1995, Lutz, FL: Psychological Assessment Resources, IncGoogle Scholar
- Meyers J, Meyers K: The Rey Complex Figure and the Recognition Trial under four different administration procedures. Clin Neuropsychol. 1995, 9: 65-67.Google Scholar
- Liberman J, Stewart W, Seines O, Gordon B: Rater agreement for the Rey-Osterrieth Complex Figure Test. J Clin Psychol. 1994, 50 (4): 615-624. 10.1002/1097-4679(199407)50:4<615::AID-JCLP2270500419>3.0.CO;2-R.PubMedGoogle Scholar
- Cherrier MM, Mendez MF, Dave M, Perryman KM: Performance on the Rey-Osterrieth Complex Figure Test in Alzheimer disease and vascular dementia. Neuropsychiatry Neuropsychol Behav Neurol. 1999, 12 (2): 95-101.PubMedGoogle Scholar
- Hsieh S, Schubert S, Hoon C, Mioshi E, Hodges JR: Validation of the Addenbrooke's Cognitive Examination III in frontotemporal dementia and Alzheimer's disease. Dement Geriatr Cogn Disord. 2013, 36 (3–4): 242-250.PubMedGoogle Scholar
- Mioshi E, Dawson K, Mitchell J, Arnold R, Hodges JR: The Addenbrooke's Cognitive Examination Revised (ACE-R): a brief cognitive test battery for dementia screening. Int J Geriatr Psychiatry. 2006, 21 (11): 1078-1085. 10.1002/gps.1610.PubMedGoogle Scholar
- Brandt J: The Hopkins verbal learning test: Development of a new memory test with six equivalent forms. Clin Neuropsychol. 1991, 5 (2): 125-142. 10.1080/13854049108403297.Google Scholar
- Shapiro AM, Benedict RH, Schretlen D, Brandt J: Construct and concurrent validity of the Hopkins Verbal Learning Test-revised. Clin Neuropsychol. 1999, 13 (3): 348-358. 10.1076/clin.13.3.348.1749.PubMedGoogle Scholar
- Woods SP, Scott JC, Conover E, Marcotte TD, Heaton RK, Grant I, HIVNRC Group: Test-retest reliability of component process variables within the Hopkins Verbal Learning Test-Revised. Assessment. 2005, 12 (1): 96-100. 10.1177/1073191104270342.PubMedGoogle Scholar
- Lacritz LH, Cullum CM, Weiner MF, Rosenberg RN: Comparison of the hopkins verbal learning test-revised to the California verbal learning test in Alzheimer's disease. Appl Neuropsychol. 2001, 8 (3): 180-184. 10.1207/S15324826AN0803_8.PubMedGoogle Scholar
- O'Neil-Pirozzi TM, Goldstein R, Strangman GE, Glenn MB: Test-re-test reliability of the Hopkins Verbal Learning Test-Revised in individuals with traumatic brain injury. Brain Inj. 2012, 26 (12): 1425-1430. 10.3109/02699052.2012.694561.PubMedGoogle Scholar
- Kaplan E, Goodglass H, Weintraub S: Boston Naming Test. 2001, Philadelphia, PA: Lippincott Williams & Wilkins, 2Google Scholar
- LaBarge E, Edwards D, Knesevich JW: Performance of normal elderly on the Boston Naming Test. Brain Lang. 1986, 27 (2): 380-384. 10.1016/0093-934X(86)90026-X.PubMedGoogle Scholar
- Knesevich JW, LaBarge E, Edwards D: Predictive value of the Boston Naming Test in mild senile dementia of the Alzheimer type. Psychiatry Res. 1986, 19 (2): 155-161. 10.1016/0165-1781(86)90008-9.PubMedGoogle Scholar
- Wechsler D: Wechsler Adult Intelligence Scale - Fourth Edition (WAIS-IV). 2008, Bloomington, MN: Pearson Education Inc.Google Scholar
- Keiser TW: Schizotype and the Wechsler Digit Span Test. J Clin Psychol. 1975, 31 (2): 303-306. 10.1002/1097-4679(197504)31:2<303::AID-JCLP2270310230>3.0.CO;2-C.PubMedGoogle Scholar
- Leung JL, Lee GT, Lam YH, Chan RC, Wu JY: The use of the Digit Span Test in screening for cognitive impairment in acute medical inpatients. Int Psychogeriatr. 2011, 23 (10): 1569-1574. 10.1017/S1041610211000792.PubMedGoogle Scholar
- Wechsler D: Test of Premorbid Functioning. UK Version (TOPF UK). 2011, Bloomington, MN: Pearson Inc.Google Scholar
- Royall DR, Cordes JA, Polk M: CLOX: an executive clock drawing task. J Neurol Neurosurg Psychiatry. 1998, 64 (5): 588-594. 10.1136/jnnp.64.5.588.PubMedPubMed CentralGoogle Scholar
- Stip E, Sepehry AA, Prouteau A, Briand C, Nicole L, Lalonde P, Lesage A: Cognitive discernible factors between schizophrenia and schizoaffective disorder. Brain Cogn. 2005, 59 (3): 292-295. 10.1016/j.bandc.2005.07.003.PubMedGoogle Scholar
- Majer M, Welberg LA, Capuron L, Miller AH, Pagnoni G, Reeves WC: Neuropsychological performance in persons with chronic fatigue syndrome: results from a population-based study. Psychosom Med. 2008, 70 (7): 829-836. 10.1097/PSY.0b013e31817b9793.PubMedGoogle Scholar
- Gau SS, Huang WL: Rapid visual information processing as a cognitive endophenotype of attention deficit hyperactivity disorder. Psychol Med. 2014, 44 (2): 435-446. 10.1017/S0033291713000640.PubMedGoogle Scholar
- Lafayette Instrument Company: Instructions and normative data for Model 32020, Purdue Pegboard. 1985, Lafayette, IN: Lafayette Instrument CompanyGoogle Scholar
- Tiffin J, Asher EI: The Purdue Pegboard: Norms and studies of reliability and validity. J Appl Psychol. 1948, 32: 234-247.PubMedGoogle Scholar
- Brown RG, Jahanshahi M, Marsden CD: The execution of bimanual movements in patients with Parkinson's, Huntington's and cerebellar disease. J Neurol Neurosurg Psychiatry. 1993, 56 (3): 295-297. 10.1136/jnnp.56.3.295.PubMedPubMed CentralGoogle Scholar
- Pernat K, Kritikos A, Phillips JG, Bradshaw JL, Iansek R, Kempster P, Bradshaw JA: The association between clinical and quantitative indexes of Parkinsonian symptomatology. Neuropsychiatry Neuropsychol Behav Neurol. 1996, 9 (4): 234-241.Google Scholar
- Flyckt L, Sydow O, Bjerkenstedt L, Edman G, Rydin E, Wiesel FA: Neurological signs and psychomotor performance in patients with schizophrenia, their relatives and healthy controls. Psychiatry Res. 1999, 86 (2): 113-129. 10.1016/S0165-1781(99)00027-X.PubMedGoogle Scholar
- Rapin I, Tourk L, Costa LD: Evaluation of the Purdue Pegboard as screening test for brain damage. Dev Med Child Neurol. 1966, 8: 45-54.PubMedGoogle Scholar
- Schmidt R, Fazekas F, Offenbacher H, Dusek T, Zach E, Reinhart B, Grieshofer P, Freidl W, Eber B, Schumacher M, Koch M, Lechner H: Neuropsychologic correlates of MRI white matter hyperintensities: a study of 150 normal volunteers. Neurology. 1993, 43 (12): 2490-2494. 10.1212/WNL.43.12.2490.PubMedGoogle Scholar
- Raz N, Gunning-Dixon FM, Head D, Dupuis JH, Acker JD: Neuroanatomical correlates of cognitive aging: evidence from structural magnetic resonance imaging. Neuropsychology. 1998, 12 (1): 95-114.PubMedGoogle Scholar
- Raz N, Lindenberger U, Rodrigue KM, Kennedy KM, Head D, Williamson A, Dahle C, Gerstorf D, Acker JD: Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. Cereb Cortex. 2005, 15 (11): 1676-1689. 10.1093/cercor/bhi044.PubMedGoogle Scholar
- Raz N, Rodrigue KM, Head D, Kennedy KM, Acker JD: Differential aging of the medial temporal lobe: a study of a five-year change. Neurology. 2004, 62 (3): 433-438. 10.1212/01.WNL.0000106466.09835.46.PubMedGoogle Scholar
- Tisdall MD, Hess AT, Reuter M, Meintjes EM, Fischl B, van der Kouwe AJ: Volumetric navigators for prospective motion correction and selective reacquisition in neuroanatomical MRI. Magn Reson Med. 2012, 68 (2): 389-399. 10.1002/mrm.23228.PubMedGoogle Scholar
- van der Kouwe AJ, Benner T, Salat DH, Fischl B: Brain morphometry with multiecho MPRAGE. Neuroimage. 2008, 40 (2): 559-569. 10.1016/j.neuroimage.2007.12.025.PubMedPubMed CentralGoogle Scholar
- Patenaude B, Smith SM, Kennedy DN, Jenkinson M: A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage. 2011, 56 (3): 907-922. 10.1016/j.neuroimage.2011.02.046.PubMedPubMed CentralGoogle Scholar
- Mori S, Zhang J: Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron. 2006, 51 (5): 527-539. 10.1016/j.neuron.2006.08.012.PubMedGoogle Scholar
- Sotiropoulos SN, Jbabdi S, Xu J, Andersson JL, Moeller S, Auerbach EJ, Glasser MF, Hernandez M, Sapiro G, Jenkinson M, Feinberg DA, Yacoub E, Lenglet C, Van Essen DC, Ugurbil K, Behrens TE, for the WU-Minn HCP Consortium: Advances in diffusion MRI acquisition and processing in the Human Connectome Project. Neuroimage. 2013, 80: 125-43.PubMedPubMed CentralGoogle Scholar
- Andersson JL, Skare S, Ashburner J: How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage. 2003, 20 (2): 870-888. 10.1016/S1053-8119(03)00336-7.PubMedGoogle Scholar
- Basser PJ, Mattiello J, LeBihan D: Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B. 1994, 103 (3): 247-254. 10.1006/jmrb.1994.1037.PubMedGoogle Scholar
- Pierpaoli C, Jezzard P, Basser PJ, Barnett A, Di Chiro G: Diffusion tensor MR imaging of the human brain. Radiology. 1996, 201 (3): 637-648.PubMedGoogle Scholar
- Behrens TE, Berg HJ, Jbabdi S, Rushworth MF, Woolrich MW: Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?. Neuroimage. 2007, 34 (1): 144-155. 10.1016/j.neuroimage.2006.09.018.PubMedGoogle Scholar
- Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL: A default mode of brain function. Proc Natl Acad Sci U S A. 2001, 98 (2): 676-682. 10.1073/pnas.98.2.676.PubMedPubMed CentralGoogle Scholar
- Chen S, Ross TJ, Zhan W, Myers CS, Chuang KS, Heishman SJ, Stein EA, Yang Y: Group independent component analysis reveals consistent resting-state networks across multiple sessions. Brain Res. 2008, 1239: 141-151.PubMedPubMed CentralGoogle Scholar
- Damoiseaux JS, Rombouts SA, Barkhof F, Scheltens P, Stam CJ, Smith SM, Beckmann CF: Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci U S A. 2006, 103 (37): 13848-13853. 10.1073/pnas.0601417103.PubMedPubMed CentralGoogle Scholar
- Fukunaga M, Horovitz SG, de Zwart JA, van Gelderen P, Balkin TJ, Braun AR, Duyn JH: Metabolic origin of BOLD signal fluctuations in the absence of stimuli. J Cereb Blood Flow Metab. 2008, 28 (7): 1377-1387. 10.1038/jcbfm.2008.25.PubMedGoogle Scholar
- Goldman RI, Stern JM, Engel J, Cohen MS: Simultaneous EEG and fMRI of the alpha rhythm. Neuroreport. 2002, 13 (18): 2487-2492. 10.1097/00001756-200212200-00022.PubMedPubMed CentralGoogle Scholar
- Kannurpatti SS, Biswal BB, Kim YR, Rosen BR: Spatio-temporal characteristics of low-frequency BOLD signal fluctuations in isoflurane-anesthetized rat brain. Neuroimage. 2008, 40 (4): 1738-1747. 10.1016/j.neuroimage.2007.05.061.PubMedGoogle Scholar
- Vincent JL, Patel GH, Fox MD, Snyder AZ, Baker JT, Van Essen DC, Zempel JM, Snyder LH, Corbetta M, Raichle ME: Intrinsic functional architecture in the anaesthetized monkey brain. Nature. 2007, 447 (7140): 83-86. 10.1038/nature05758.PubMedGoogle Scholar
- Feinberg DA, Moeller S, Smith SM, Auerbach E, Ramanna S, Gunther M, Glasser MF, Miller KL, Ugurbil K, Yacoub E: Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging. PloS One. 2010, 5 (12): e15710-10.1371/journal.pone.0015710.PubMedPubMed CentralGoogle Scholar
- Moeller S, Yacoub E, Olman CA, Auerbach E, Strupp J, Harel N, Ugurbil K: Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. Magn Reson Med. 2010, 63 (5): 1144-1153. 10.1002/mrm.22361.PubMedPubMed CentralGoogle Scholar
- Beckmann CF, DeLuca M, Devlin JT, Smith SM: Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci. 2005, 360 (1457): 1001-1013. 10.1098/rstb.2005.1634.PubMedPubMed CentralGoogle Scholar
- Jenkinson M: Fast, automated, N-dimensional phase-unwrapping algorithm. Magn Reson Med. 2003, 49 (1): 193-197. 10.1002/mrm.10354.PubMedGoogle Scholar
- Salimi-Khorshidi G, Douaud G, Beckmann CF, Glasser MF, Griffanti L, Smith SM: Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. Neuroimage. 2014, 90: 449-468.PubMedPubMed CentralGoogle Scholar
- Griffanti L, Salimi-Khorshidi G, Beckmann CF, Auerbach EJ, Douaud G, Sexton CE, Zsoldos E, Ebmeier KP, Filippini N, Mackay CE, Moeller S, Xu J, Yacoub E, Baselli G, Ugurbil K, Miller KL, Smith SM: ICA-based artefact removal and accelerated fMRI acquisition for improved Resting State Network imaging. Neuroimage. 2014, 95C: 232-247.Google Scholar
- Greve DN, Fischl B: Accurate and robust brain image alignment using boundary-based registration. Neuroimage. 2009, 48 (1): 63-72. 10.1016/j.neuroimage.2009.06.060.PubMedPubMed CentralGoogle Scholar
- Filippini N, MacIntosh BJ, Hough MG, Goodwin GM, Frisoni GB, Smith SM, Matthews PM, Beckmann CF, Mackay CE: Distinct patterns of brain activity in young carriers of the APOE-epsilon4 allele. Proc Natl Acad Sci U S A. 2009, 106 (17): 7209-7214. 10.1073/pnas.0811879106.PubMedPubMed CentralGoogle Scholar
- Smith SM, Fox PT, Miller KL, Glahn DC, Fox PM, Mackay CE, Filippini N, Watkins KE, Toro R, Laird AR, Beckmann CF: Correspondence of the brain's functional architecture during activation and rest. Proc Natl Acad Sci U S A. 2009, 106 (31): 13040-13045. 10.1073/pnas.0905267106.PubMedPubMed CentralGoogle Scholar
- Beckmann CF, Smith SM: Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans Med Imaging. 2004, 23 (2): 137-152. 10.1109/TMI.2003.822821.PubMedGoogle Scholar
- Hui JS, Wilson RS, Bennett DA, Bienias JL, Gilley DW, Evans DA: Rate of cognitive decline and mortality in Alzheimer's disease. Neurology. 2003, 61 (10): 1356-1361. 10.1212/01.WNL.0000094327.68399.59.PubMedGoogle Scholar
- Sullivan P, Pary R, Telang F, Rifai AH, Zubenko GS: Risk factors for white matter changes detected by magnetic resonance imaging in the elderly. Stroke. 1990, 21 (10): 1424-1428. 10.1161/01.STR.21.10.1424.PubMedGoogle Scholar
- DeCarli C, Murphy DG, Tranh M, Grady CL, Haxby JV, Gillette JA, Salerno JA, Gonzales-Aviles A, Horwitz B, Rapoport SI, Schapiro MB: The effect of white matter hyperintensity volume on brain structure, cognitive performance, and cerebral metabolism of glucose in 51 healthy adults. Neurology. 1995, 45 (11): 2077-2084. 10.1212/WNL.45.11.2077.PubMedGoogle Scholar
- Garde E, Lykke Mortensen E, Rostrup E, Paulson OB: Decline in intelligence is associated with progression in white matter hyperintensity volume. J Neurol Neurosurg Psychiatry. 2005, 76 (9): 1289-1291. 10.1136/jnnp.2004.055905.PubMedPubMed CentralGoogle Scholar
- Fazekas F, Chawluk JB, Alavi A, Hurtig HI, Zimmerman RA: MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging. AJR Am J Roentgenol. 1987, 149 (2): 351-356. 10.2214/ajr.149.2.351.PubMedGoogle Scholar
- Cordonnier C, Al-Shahi Salman R, Wardlaw J: Spontaneous brain microbleeds: systematic review, subgroup analyses and standards for study design and reporting. Brain. 2007, 130 (Pt 8): 1988-2003.PubMedGoogle Scholar
- Cordonnier C, Potter GM, Jackson CA, Doubal F, Keir S, Sudlow CL, Wardlaw JM, Al-Shahi Salman R: Improving interrater agreement about brain microbleeds: development of the Brain Observer MicroBleed Scale (BOMBS). Stroke. 2009, 40 (1): 94-99. 10.1161/STROKEAHA.108.526996.PubMedGoogle Scholar
- Poels MM, Ikram MA, van der Lugt A, Hofman A, Niessen WJ, Krestin GP, Breteler MM, Vernooij MW: Cerebral microbleeds are associated with worse cognitive function: the Rotterdam Scan Study. Neurology. 2012, 78 (5): 326-333. 10.1212/WNL.0b013e3182452928.PubMedGoogle Scholar
- Schneider JA: Brain microbleeds and cognitive function. Stroke. 2007, 38 (6): 1730-1731. 10.1161/STROKEAHA.107.487173.PubMedGoogle Scholar
- Wardlaw JM, Bastin ME, Valdes Hernandez MC, Maniega SM, Royle NA, Morris Z, Clayden JD, Sandeman EM, Eadie E, Murray C, Starr JM, Deary IJ: Brain aging, cognition in youth and old age and vascular disease in the Lothian Birth Cohort 1936: rationale, design and methodology of the imaging protocol. Int J Stroke. 2011, 6 (6): 547-559. 10.1111/j.1747-4949.2011.00683.x.PubMedGoogle Scholar
- Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, Watkins KE, Ciccarelli O, Cader MZ, Matthews PM, Behrens TE: Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage. 2006, 31 (4): 1487-1505. 10.1016/j.neuroimage.2006.02.024.PubMedGoogle Scholar
- Block J, Kremen AM: IQ and ego-resiliency: conceptual and empirical connections and separateness. J Pers Soc Psychol. 1996, 70 (2): 349-361.PubMedGoogle Scholar
- Frodl T, Carballedo A, Fagan AJ, Lisiecka D, Ferguson Y, Meaney JF: Effects of early-life adversity on white matter diffusivity changes in patients at risk for major depression. J Psychiatry Neurosci. 2012, 37 (1): 37-45. 10.1503/jpn.110028.PubMedPubMed CentralGoogle Scholar
- Filippini N, Nickerson LD, Beckmann CF, Ebmeier KP, Frisoni GB, Matthews PM, Smith SM, Mackay CE: Age-related adaptations of brain function during a memory task are also present at rest. Neuroimage. 2012, 59 (4): 3821-3828. 10.1016/j.neuroimage.2011.11.063.PubMedGoogle Scholar
- Stringhini S, Sabia S, Shipley M, Brunner E, Nabi H, Kivimaki M, Singh-Manoux A: Association of socioeconomic position with health behaviors and mortality. JAMA. 2010, 303 (12): 1159-1166. 10.1001/jama.2010.297.PubMedPubMed CentralGoogle Scholar
- Hyde M, Wiggins RD, Higgs P, Blane DB: A measure of quality of life in early old age: the theory, development and properties of a needs satisfaction model (CASP-19). Aging Ment Health. 2003, 7 (3): 186-194. 10.1080/1360786031000101157.PubMedGoogle Scholar
- Singh-Manoux A, Richards M, Marmot M: Leisure activities and cognitive function in middle age: evidence from the Whitehall II study. J Epidemiol Community Health. 2003, 57 (11): 907-913. 10.1136/jech.57.11.907.PubMedPubMed CentralGoogle Scholar
- Keating BJ, Tischfield S, Murray SS, Bhangale T, Price TS, Glessner JT, Galver L, Barrett JC, Grant SF, Farlow DN, Chandrupatla HR, Hansen M, Ajmal S, Papanicolaou GJ, Guo Y, Li M, Derohannessian S, de Bakker PI, Bailey SD, Montpetit A, Edmondson AC, Taylor K, Gai X, Wang SS, Fornage M, Shaikh T, Groop L, Boehnke M, Hall AS, Hattersley AT, et al: Concept, design and implementation of a cardiovascular gene-centric 50 k SNP array for large-scale genomic association studies. PloS One. 2008, 3 (10): e3583-10.1371/journal.pone.0003583.PubMedPubMed CentralGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-244X/14/159/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.