Study
Age (years)
MRI Brain
+ ALSPAC
18–21
500
+ Generation R: Wave 1
5–7
1,000
+ Generation R: Wave 2
10–12
5,000*
IMAGEN
13.5–15.5
2,000
+ NFBC1986
25–27
900
NIH-PD
0–18
500
NIMH-CHPB
3–25
400
Saguenay Youth Study
12–18
1,024
AGES (REYKJAVIK)
>70
5,000
ARIC
55–74
1,700
Framingham Heart
38–88
2,500
Health ABC
70–79
500
+ Lothian birth cohort
>70
700
MESA
45–84
1,000
PURE-MIND
60
800
Rotterdam Study
60–90
5,000
Saguenay Parent Study
40–60
900
Given our interest in the developmental origin of health and disease, I will illustrate these by describing seven cohorts, spanning the first three decades of life (Table 8.1, top).
As described below, in some cases, the acquisition of MR images was the sole purpose for establishing a cohort (Sect. 8.2.1); naturally, with the variety of motivations behind these efforts comes a variety of designs (Sect. 8.2.2). In other instances, MR was used in a subset of participants of an already existing birth cohort (Sect. 8.2.3).
8.2.1 MR-Based Cohorts
Let me begin by describing four developmental cohorts designed right from the outset as MR-based. I will do so in the order in which they started their data collection; this chronology is reflected, to some extent, in their design, as summarized in Table 8.2.
Table 8.2
An overview of four population-based studies of brain development
NIMH-CHPB | NIH-PD | SYS | IMAGEN | |
---|---|---|---|---|
Sample (n) | 400 | 500 | 1,024 | 2,000 |
Age range | 3–25 years | 7 days–18 years | 12–18 years | 14 years |
Design | Longitudinal, multi-ethnic population | Longitudinal, multi-ethnic population | Cross-sectional, founder population | Cross-sectional, multi-ethnic population |
Recruitment | Local community | Population sampling (census-based targets) | High schools | High schools |
Genetics | Candidate genes | None | DNA (adolescents, biological parents) | DNA (adolescents) |
Environment | Socioeconomic status | Socioeconomic status | Socioeconomic status | Socioeconomic status |
Pregnancy (smoking, alcohol, drugs) | Pregnancy (smoking, alcohol, drugs) | Pregnancy (smoking, alcohol, drugs) | ||
Stressful life events | ||||
Infancy (breast-feeding) | ||||
Childhood (maternal care, stressful life events, food availability/variety) Adolescents (diet, sleep) | ||||
Imaging | T1 W images, 1.5-mm-thick axial slices, | T1W, T2W, PDW images, T1 and T2 relaxometry, 1.5T GE and Siemens scanners | Brain: T1 W, T2 W, PDW images, MTR; Abdomen: fat, kidney volume; 1.0T Philips scanner | Structural: T1 W images, DTI; functional: face task, MID task, stop-signal task, global-cognition Task; 3-T scanners (GE, Philips, Siemens, Bruker) |
1.5T GE scanner | ||||
Behaviour/ Cognition | Child and Parent diagnostic (psychiatric) Interview for children | DISC | DISC Predictive Scale (psychiatric symptoms) | DAWBA and SDQ (psychiatric symptoms) |
Child behaviour checklist | Child behaviour checklist | Positive youth development, personality (NEO-PI), Anti-social behaviour | Personality (NEO FFI, TCI-R) | |
Intelligence (WISC-III/WISC-IV subtests) | Personality (TCI) | Drug experimentation, Sleep, Sexuality | Substance use (SUPRS, ESPAD, DAST, AUDIT, MAST, FTND, TLFB) | |
Spatial working memory, | Intelligence (WASI, WISC-III subtests) | Intelligence (WISC-III) | Intelligence (WISC subscales) | |
Go/No-Go task | ||||
Academic skills (reading, spelling) | Memory (CVLT) | Memory (CMS) | Executive functions (CANTAB) | |
Grooved Pegboard and Handedness | Executive functions (CANTAB, NEPSY) | Executive functions (e.g. Stroop, Fluency, Working Memory, Attention) | Face perception | |
Academic skills (calculation, passage comprehension, letter word) | Face perception, body-image perception | Handedness and Fine motor skills | ||
Handedness and Fine motor skills | Reward/Impulsivity | |||
Phonological processing | ||||
Academic skills (math, math fluency, reading, spelling) and number sense | ||||
Handedness and Fine motor skills |
8.2.1.1 National Institutes of Mental Health—Child Psychiatry Branch Cohort
This cohort was set up in 1989 as a normative study of brain structure during childhood and adolescence. It has been carried out at one acquisition site: Bethesda, Maryland, U.S.A. One of the primary goals of the study has been the comparison of normative data with MR images acquired in parallel studies of childhood psychiatric disorders, including conditions both common (e.g. attention deficit hyperactivity disorder, ADHD) and rare (e.g. childhood-onset schizophrenia). It is a longitudinal study, with the participants’ ages at the time of initial recruitment ranging from 3 to 25 years, and the visits repeated in two- to four-year intervals (e.g. Giedd et al. 1996; Lenroot et al. 2007). Given that this was the very first large-scale MR study of brain structure in typically developing children and adolescents, it was designed primarily as an MR study, with little data collected on genetic and environmental exposures other than the socioeconomic status (SES) of the families; DNA collection was added at a later point. Behavioural and cognitive assessments are not extensive (see Table 8.2, first column). The neuroimaging protocol includes T1-weighted images: DTI and functional MRI were added at later stages in a subset of participants. The sample comes primarily from the local community. In terms of the recruitment strategy, this study has relied to a great extent on the interest of NIH employees working on the Bethesda campus. As a consequence, this is a multi-ethnic sample, with an average estimated IQ of 113.
8.2.1.2 National Institutes of Health—Pediatric Database
This project was initiated in the mid-2000 as a multi-centre study where MR acquisition took place at six sites in the U S A. It is also a normative study of brain development that complements the NIH-CHB cohort in two important ways: (1) by including a large group of infants and young children (age 7 days–4 years) and (2) by adding other (structural) MR sequences (Evans 2006). It is a longitudinal study, with the older children (5–18 years) scanned three times in two-year intervals and the younger children (7 days–4 years) scanned up to five times, with intervals as short as 3 months. No genetic data were collected.
Assessment of environmental exposures was limited to variables related to SES and prenatal exposures (e.g. cigarettes, alcohol and drugs). Behavioural and cognitive assessments were more extensive than those in the NIH-CHB cohort (see Table 8.2, second column and Waber et al. 2007). The NIH-PD project included several MR sequences: T1-, T2- and PD-weighted images, as well as T1 and T2 (single slice) relaxometry, were acquired in all participants. Diffusion tensor images and MR spectroscopy were acquired at a subset of the acquisition sites.
The sample was ascertained through population-based sampling: each of the six acquisition sites recruited participants using site-specific demographic targets calculated according to the U.S. Census 2000 data. The resulting sample is multi-ethnic and includes a wide range of SES characteristics. The average IQ is 110.
8.2.1.3 Saguenay Youth Study
The Saguenay Youth Study was initiated in the mid-2000 as an investigation of genetic and environmental factors shaping the adolescent brain and body. MR acquisition took place at a single site in Canada. Adolescents (12–18 years) and their biological parents were recruited from a population with a known genetic founder effect (Text Box 8.2.), in the population of the Saguenay Lac-Saint-Jean (SLSJ) region of Quebec, Canada.
Text Box 8.2. Genetic founder effect
Founder populations provide important advantages for genetic and epidemiological research. Compared with the outbred populations constituting most of today’s migratory world, they are more homogenous in genetic background and, due to their relative geographical isolation, also in environmental exposures such as cultural habits, diet, climate (Peltonen et al. 2000). Because of this genetic homogeneity, fewer genes and gene variants are thought to contribute to the phenotypic expression of complex genetic traits (De Braekeleer 1991). This founder effect, combined with more uniform environmental exposures, is expected to increase the likelihood of identifying genes underlying complex genetic traits.
At its inception, this was a cross-sectional study. Multiple quantitative phenotypes relevant to mental, cardiovascular and metabolic health were acquired, using an extensive 15 h protocol spread over several days (Pausova et al. 2007). A follow-up of the participants will begin in 2014. By design, half of the participants were exposed to maternal cigarette smoking (the cases), while the other, non-exposed half has been matched to them by maternal education (the controls). A family-based design was used, with adolescent siblings fully phenotyped. Their biological parents provided only a blood sample for genetic analyses and answered questions about their current mental health. (The full MR-based assessment of the parents is in progress).
Recruitment took place in high schools across the SLSJ region. Samples of DNA were collected in all adolescents and their biological parents. Assessment of environmental exposures covered the prenatal period (e.g. smoking, alcohol), infancy (e.g. breastfeeding), childhood (e.g. food availability, maternal care, stressful life events) and adolescence (e.g. diet, sleep). Behavioural and cognitive assessments were extensive and included both self-reported psychiatric symptoms, components of positive youth development and personality, as well as a thorough 6 hour assessment of cognitive abilities (see Table 8.2, third column and Pausova et al. 2007; Kafouri et al. 2009). In addition, all adolescents were assessed with a detailed cardiovascular and metabolic protocol. MR sequences included T1-weighted, T2-weighted and PD-weighted images, magnetization transfer (MT) images (as an index of myelination) and abdominal images (extra- and intraabdominal fat, kidney volume). The sample is of a single ethnicity (white Caucasians) and, given the 50 % inclusion rate of adolescents born to mothers who smoked during pregnancy and the matching procedure, it is of lower SES than the general population of the region. The average IQ is 105.
8.2.1.4 IMAGEN
This project started in 2007 as a multi-centre, cross-sectional study of the genetic and neurobiological bases of individual variability in impulsivity, reinforcer sensitivity and emotional reactivity. MR acquisition took place at eight acquisition sites located in the United Kingdom, Ireland, France and Germany (Schumann et al. 2010). Adolescents (14 years old) were recruited primarily through local high schools. Samples of DNA were collected in all participating adolescents. Assessment of environmental exposures was limited to the main SES characteristics, stressful life events and prenatal exposures (e.g. smoking, alcohol). Behavioural and cognitive assessments included a basic assessment of cognition and a detailed assessment of the main outcomes of interest, namely impulsivity, reward processing and substance use (see Table 8.2, fourth column). MR sequences included structural (T1-weighted, DTI) and functional imaging, with the latter consisting of four paradigms: a Face Task, a Monetary Incentive Delay (MID) Task, a Stop-Signal Task and a Global-Cognition Task. At 16 years of age, about 80 % of the original sample completed a web-based follow-up of mental health and substance use. At 18 years of age, all adolescents will be invited for a follow-up visit that will include the same structural and functional MR imaging acquired at 14 years of age. The sample is multi-ethnic, with a wide range of the parental education level. The average estimated IQ is 108.
8.2.2 Comparison of the Four Cohorts: Recruitment and Assessments
Given the variety of primary goals and the time of inception, these four studies of brain development range significantly in their approaches to the selection and recruitment of typically developing children and/or adolescents from the local population, the collection of genetic and environmental variables and the inclusion of specific MR sequences.
In the case of selection and recruitment, the NIH-CHB and NIH-PD studies represent two extremes, with the former being a convenience sample and the latter a stratified random sample. It should be pointed out, however, that using a random-sampling strategy does not necessarily yield a random sample in the final dataset. This is chiefly due to two reasons. First, given the volunteer nature of neuroimaging studies, there is a self-selection bias in the initial stage of recruitment. Second, depending on the stringency of the exclusion criteria, a varied number of potential participants are excluded from the final sample, thus creating a “super-healthy” cohort. In this respect, the NIH-CHB and NIH-PD studies are more exclusionary than the SYS and IMAGEN studies. Note that this strategy may reduce the heterogeneity of the sample vis-à-vis exposures associated with negative outcomes in early age. Overall, the true randomness and representativeness of population-based samples are difficult to achieve in studies requiring significant commitment of the participants; with adequate resources, this limitation can be mitigated to some extent by compensating participants for their time and inconvenience.
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