There is a bidirectional relationship between the brain and nutrition. The brain influences our food

choices and, therefore, the amount of macro- and micro-nutrients entering our bodies. These nutrients

affect body composition and a variety of metabolic processes that, in turn, influence brain function and

structure. Understanding the various elements of this brain-body loop is one of the overarching goals of

the proposed research, both in terms of gaining new knowledge about the underlying processes and

applying it to enhance population health.

We will take advantage of several community-based cohorts including ALSPAC to investigate bidirectional

brain-body relationships in the context of food choices; magnetic resonance images (MRIs) of

the brain and, in most cases, of abdominal fat have been collected in these cohorts together with a wealth

of other relevant information (e.g., food recall interviews) and blood samples suitable for genetic and

metabolomics analyses.

1. Cohorts

Given the high cost of acquiring large samples with imaging-based, systems-level phenotypes, we have

brought together several cohorts in which MRI of the brain and body have been already accomplished or

funded (~8,000 participants). Three of these samples are birth cohorts (ALSPAC, Generation R, and

NFBC86), thus allowing us to use all of the longitudinal data collected so far. In addition, we will be able

to incorporate the (Canadian) Saguenay Family Study, which is unique with regards to its twogenerational

design (adolescents and their middle-aged parents) and the detailed cardiovascular and

metabolic phenotyping available in all participants (15 hours of assessments).

Using these datasets, we will ask: (1) how do inter-individual differences in the structural (and functional)

properties of the relevant neural circuits relate to food choices, such as fat or carbohydrate intake; and (2)

how do inter-individual differences in body composition (visceral fat from MRI and/or body composition

from bioimpedance) influence metabolic profiles (as determined by metabolomics) and, in turn, brain

structure and function.

2. Magnetic resonance imaging

All cohorts acquired T1-weighted images of the brain, which are well suited for the quantification of a

number of grey-matter properties (see below). Furthermore, all cohorts used MR sequences (Diffusion

Tensor Images [DTI] and/or Magnetization Transfer Images [MTI]) suitable for quantifying various

properties of white matter. Three of the cohorts (Generation-R, NFBC86, and the parent arm of the SFS)

have acquired resting-state functional MRI (fMRIrs), and one of the cohorts (ALSPAC) has acquired a

paradigm-based fMRI (viewing faces; Grosbras and Paus 2006).

Using this rich dataset, we plan to use image-processing pipelines developed and implemented at two of

the sites (Erasmus University and University of Toronto) to process these images in order to generate

quantitative phenotypes suitable for answering both questions, namely from the brain to food choices and

from metabolic profiles (and body composition) to the brain. These brain MR phenotypes include:

MRI sequence Structure and physiology

T1-weighted Volumes, thickness, folding, shape, tissue density

Diffusion tensor imaging Fractional anisotropy, mean diffusivity, track delineation

Magnetization transfer Myelination index

Resting state functional Spontaneous cerebral networks; functional connectivity

Paradigm-based functional Brain response associated with specific stimuli/tasks; functional

connectivity.