Population neuroscience: why and how

Abstract

Population neuroscience endeavours to identify environmental and genetic factors that shape the function and structure of the human brain; it uses tools and knowledge of genetics, epidemiology, and cognitive neuroscience. Here, I focus on the application of population neuroscience in studies of brain development. By describing in some detail four existing large-scale magnetic resonance (MR) imaging studies of typically developing children and adolescents, I provide an overview of their design, including population sampling and recruitment, assessments of environmental and genetic "exposures," and measurements of brain and behavior "outcomes." I then discuss challenges faced by investigators carrying out such MR-based studies, including quality assurance, quality control and intersite coordination, and provide a brief overview of the achievements made so far. I conclude by outlining future directions vis-à-vis population neuroscience, such as design strategies that can be used to evaluate the presence of absence of causality in associations discovered by observational studies.

Figure 1

Population neuroscience. Traditionally, epidemiology and genetics attempt to identify “exposures” in the individual's environment and genes, respectively, as factors affecting the health of populations. Cognitive neuroscience provides the knowledge of brain and behavior “outcomes” that, ultimately, inform about possible pathways leading to good or ill mental health.

Figure 2

Sample size in genome‐wide association studies. Total individuals required for 80% power. The computations assume the number of cases equals the number of controls and a genotype relative‐risk of 1.75. CEU, JPT+CHB, and YRI are the HapMap populations. Affy 250 K Nsp and Affy 250 K Sty represent the two chips that make up the Affymetrix 500 K genotyping system. CEU, Utah residents with ancestry from northern and western Europe; JPT, Japanese in Tokyo, Japan; CHB, Han Chinese in Beijing, China; YRI, Yoruba in Ibadan, Nigeria. (Reproduced with permission from Klein, BMC Genet, 2007, 8, 58, ©).

Figure 3

Within‐subject Time 1 to Time 2 correlations in brain and behavior. These plots illustrate the degree of similarity in structural and functional MRI measures and a behavioral measure across two time‐points. The data were collected in the same set of children at Time 1 (10 years of age) and Time 2 (11.5 years of age) in a longitudinal study carried out in the author's laboratory at the Montreal Neurological Institute. WM volume: white‐matter volume [in mm³] in the right frontal lobe; BOLD Response: mean BOLD response [in %change] to angry faces in the left amygdala; reaction time: reaction time [in s] in a task requiring discrimination of affect in faces. Note that the variance between the Time 1 and Time 2 measurements is due to both measurement errors and developmental changes. Direct comparisons of Time 1 and Time 2 data suggest, however, that only the behavioral data show statistically significant (developmental) effect of visit in these three examples.

Figure 4

Outcomes: brain structure. Various quantitative measures of brain structure can be derived from T1‐weighted images using automatic image‐processing pipelines. These include 3D maps of white‐ and gray‐matter “densities,” deformation fields capturing differences in local shapes, segmented volumes of different brain structures (e.g. hippocampus), cortical thickness and folding. Reprinted from Paus 2005.

Figure 5

Outcomes: brain function. Functional MR images provide a variety of measures, such as the mean BOLD response in various brain regions (A) or estimates of functional connectivity (B). Examples shown here are based on fMRI data acquired in the author's laboratory in 10‐year‐old children watching video‐clips of faces (A) and hands (B). Regions included in the correlation matrix were identified with partial least squares. PMC, premotor cortex; PFC, prefrontal cortex, STS, superior temporal sulcus; FFA, face fusiform area; CeA, central amygdala; SLEA, sublenticular extended amygdala. Circles indicate brain regions identified by labels above each brain image. Plots shown in (B) are reprinted from Grosbras et al. [ 2007].

Figure 6

Sensor augmented costume. A drawing illustrating ideas for the use of sensors for data gathering generated by a participant of a workshop organized by the Stanford Center for Innovations in Learning. Reprinted from Pea et al., 2004.

Figure 7

Population neuroscience and the developing brain. Population neuroscience integrates research on pathways underlying effects of environmental and genetic “exposures” on brain and behavior “outcomes” throughout the individual's life and across generations.