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Brain imaging and human nutrition: which measures to use in intervention studies?

Published online by Cambridge University Press:  01 August 2013

Stéphane V. Sizonenko
Affiliation:
Division of Development and Growth, Department of Child and Adolescent Medicine, School of Medicine and University Hospital, Geneva, Switzerland
Claudio Babiloni
Affiliation:
Department of Molecular Medicine, University of Rome ‘La Sapienza’, Rome, Italy IRCCS San Raffaele Pisana, Rome, Italy
Eveline A. de Bruin
Affiliation:
Unilever R&D Vlaardingen, Vlaardingen, The Netherlands
Elizabeth B. Isaacs
Affiliation:
Childhood Nutrition Research Centre, UCL Institute of Child Health, London, UK
Lena S. Jönsson
Affiliation:
ILSI Europe a.i.s.b.l., Avenue E. Mounier 83, Box 6, B-1200Brussels, Belgium
David O. Kennedy
Affiliation:
Brain, Performance and Nutrition Research Centre, Northumbria University, Newcastle, UK
Marie E. Latulippe
Affiliation:
ILSI Europe a.i.s.b.l., Avenue E. Mounier 83, Box 6, B-1200Brussels, Belgium
M. Hasan Mohajeri
Affiliation:
DSM Nutritional Products, R&D Human Nutrition and Health, Basel, Switzerland
Judith Moreines
Affiliation:
Pfizer Consumer Healthcare, 5 Giralda Farms, Madison, NJ, USA
Pietro Pietrini
Affiliation:
Laboratory of Clinical Biochemistry and Molecular Biology, University of Pisa Medical School, Pisa, Italy
Kristine B. Walhovd
Affiliation:
Psychology Department, Center for the Study of Human Cognition, University of Oslo, Oslo, Norway
Robert J. Winwood
Affiliation:
DSM Nutritional Products (UK) Limited, Delves Road, Heanor, Derbyshire, UK
John W. Sijben
Affiliation:
Nutricia Advanced Medical Nutrition, Danone Research Centre for Specialised Nutrition, Wageningen, The Netherlands
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Abstract

The present review describes brain imaging technologies that can be used to assess the effects of nutritional interventions in human subjects. Specifically, we summarise the biological relevance of their outcome measures, practical use and feasibility, and recommended use in short- and long-term nutritional studies. The brain imaging technologies described consist of MRI, including diffusion tensor imaging, magnetic resonance spectroscopy and functional MRI, as well as electroencephalography/magnetoencephalography, near-IR spectroscopy, positron emission tomography and single-photon emission computerised tomography. In nutritional interventions and across the lifespan, brain imaging can detect macro- and microstructural, functional, electrophysiological and metabolic changes linked to broader functional outcomes, such as cognition. Imaging markers can be considered as specific for one or several brain processes and as surrogate instrumental endpoints that may provide sensitive measures of short- and long-term effects. For the majority of imaging measures, little information is available regarding their correlation with functional endpoints in healthy subjects; therefore, imaging markers generally cannot replace clinical endpoints that reflect the overall capacity of the brain to behaviourally respond to specific situations and stimuli. The principal added value of brain imaging measures for human nutritional intervention studies is their ability to provide unique in vivo information on the working mechanism of an intervention in hypothesis-driven research. Selection of brain imaging techniques and target markers within a given technique should mainly depend on the hypothesis regarding the mechanism of action of the intervention, level (structural, metabolic or functional) and anticipated timescale of the intervention's effects, target population, availability and costs of the techniques.

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Type
Full Papers
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - SA
The online version of this article is published within an Open Access environment subject to the conditions of the Creative Attribution-NonCommercial-ShareAlike licence
Copyright
Copyright © ILSI Europe [2013]
Figure 0

Table 1 Summary of the key features of imaging measurements

Figure 1

Fig. 1 1H-MRS and 31P-MRS at 1·5 T. (a) Normal typical spectrum of proton spectroscopy of grey matter and changes during brain development. (a1) Normal preterm infant born at 29 weeks of gestation (scan at 35 weeks), (a2) normal-term newborn, (a3) normal infant of 6 months and (a4) normal adult. (b) Normal typical spectrum of 31P spectroscopy of grey matter and changes during brain development. (b1) Normal preterm infant born at 29 weeks of gestation (scan at 35 weeks), (b2) normal-term newborn, (b3) normal infant of 6 months and (b4) normal adult. Cho, choline; Cr, creatine; NAA, N-acetylaspartate; Lac, lactate; PME, phosphomonoesters; Pi, inorganic phosphates; PCr, phosphocreatine; PDE, phosphodiesters; NTP, nucleotide triphosphate. Reproduced from Robertson & Cox(254).

Figure 2

Fig. 2 Whole-brain segmentation of a T1-weighted scan as implemented in FreeSurfer (a brain imaging software package)(24). The segmentation is shown in (a) coronal, (b) horizontal and (c) sagittal views. Each image element (voxel) of the brain volume is labelled as belonging to different structures. For instance, the hippocampus is labelled in yellow.

Figure 3

Fig. 3 From diffusion tensor imaging, measures of fractional anisotropy (FA) can be derived. Areas of higher diffusion directionality, or FA, are shown in a lighter colour (a) coronally and (b) horizontally. The white matter skeleton created by tract-based spatial statistics as implemented in FSL(41) contains only tract voxels common to all participants in a study and is shown here in green imposed on the FA volume. To the right (c), the direction of diffusion in different parts of the brain is shown colour coded. Red colour denotes diffusion along the medial–lateral axis (such as in the corpus callosum connecting the hemispheres). Green colour denotes diffusion along the posterior–anterior axis, while blue colour denotes diffusion along the inferior–superior axis.

Figure 4

Table 2 Absolute concentrations of brain metabolites in individuals of different age groups in mmol/kg brain tissue and significance tests for differences found* (Mean values with their standard errors)

Figure 5

Table 3 Concentrations of 31P metabolites in healthy brains of human neonates, infants and adults† (Mean values and standard deviations)

Figure 6

Fig. 4 Analysis in the time and frequency domains of electroencephalographic (EEG) data related to a motor event (i.e. voluntary self-paced right middle finger extensions). (a) A schematic representation of ongoing EEG rhythms at α frequencies (about 10 Hz) before, during and after the onset of the electromyographic (EMGo) activity associated with voluntary self-paced middle finger extensions. It can be seen that the amplitude of alpha rhythms is reduced during the preparation and execution of the movement, the so-called alpha event-related desynchronisation (ERD) and is enhanced after the EMGo, the so-called event-related synchronisation (ERS). In the same dataset, a slow negative shift is hidden in the EEG oscillations, namely the movement event-related potentials (MRP). The example shows that the same EEG dataset can be analysed in the frequency domain to compute the alpha ERD and in the time domain to produce MRP. (b) A topographic map showing cortical sources of the MRP as computed by a weighted, minimum-norm linear inverse estimation. It can be noted that the maximum source amplitude (red hot spot) is represented in the Rolandic region of the left hemisphere contralateral to the movement side. (c, d) Topographic maps showing cortical sources of the ERD at α (about 10 Hz) and β (about 20 Hz) frequencies. With respect to the MRP, alpha and beta ERD were characterised by maximum source amplitude (red hot spots) in the Rolandic regions of both hemispheres. It is concluded that quantitative EEG techniques can reveal parallel physiological processes underlying the activation of sensorimotor cortical regions related to voluntary movements.