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      Good practice in food-related neuroimaging

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          ABSTRACT

          The use of neuroimaging tools, especially functional magnetic resonance imaging, in nutritional research has increased substantially over the past 2 decades. Neuroimaging is a research tool with great potential impact on the field of nutrition, but to achieve that potential, appropriate use of techniques and interpretation of neuroimaging results is necessary. In this article, we present guidelines for good methodological practice in functional magnetic resonance imaging studies and flag specific limitations in the hope of helping researchers to make the most of neuroimaging tools and avoid potential pitfalls. We highlight specific considerations for food-related studies, such as how to adjust statistically for common confounders, like, for example, hunger state, menstrual phase, and BMI, as well as how to optimally match different types of food stimuli. Finally, we summarize current research needs and future directions, such as the use of prospective designs and more realistic paradigms for studying eating behavior.

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          Most cited references73

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          Self-control in decision-making involves modulation of the vmPFC valuation system.

          Every day, individuals make dozens of choices between an alternative with higher overall value and a more tempting but ultimately inferior option. Optimal decision-making requires self-control. We propose two hypotheses about the neurobiology of self-control: (i) Goal-directed decisions have their basis in a common value signal encoded in ventromedial prefrontal cortex (vmPFC), and (ii) exercising self-control involves the modulation of this value signal by dorsolateral prefrontal cortex (DLPFC). We used functional magnetic resonance imaging to monitor brain activity while dieters engaged in real decisions about food consumption. Activity in vmPFC was correlated with goal values regardless of the amount of self-control. It incorporated both taste and health in self-controllers but only taste in non-self-controllers. Activity in DLPFC increased when subjects exercised self-control and correlated with activity in vmPFC.
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            Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI.

            We proposed ICA-AROMA as a strategy for the removal of motion-related artifacts from fMRI data (Pruim et al., 2015). ICA-AROMA automatically identifies and subsequently removes data-driven derived components that represent motion-related artifacts. Here we present an extensive evaluation of ICA-AROMA by comparing our strategy to a range of alternative strategies for motion-related artifact removal: (i) no secondary motion correction, (ii) extensive nuisance regression utilizing 6 or (iii) 24 realignment parameters, (iv) spike regression (Satterthwaite et al., 2013a), (v) motion scrubbing (Power et al., 2012), (vi) aCompCor (Behzadi et al., 2007; Muschelli et al., 2014), (vii) SOCK (Bhaganagarapu et al., 2013), and (viii) ICA-FIX (Griffanti et al., 2014; Salimi-Khorshidi et al., 2014), without re-training the classifier. Using three different functional connectivity analysis approaches and four different multi-subject resting-state fMRI datasets, we assessed all strategies regarding their potential to remove motion artifacts, ability to preserve signal of interest, and induced loss in temporal degrees of freedom (tDoF). Results demonstrated that ICA-AROMA, spike regression, scrubbing, and ICA-FIX similarly minimized the impact of motion on functional connectivity metrics. However, both ICA-AROMA and ICA-FIX resulted in significantly improved resting-state network reproducibility and decreased loss in tDoF compared to spike regression and scrubbing. In comparison to ICA-FIX, ICA-AROMA yielded improved preservation of signal of interest across all datasets. These results demonstrate that ICA-AROMA is an effective strategy for removing motion-related artifacts from rfMRI data. Our robust and generalizable strategy avoids the need for censoring fMRI data and reduces motion-induced signal variations in fMRI data, while preserving signal of interest and increasing the reproducibility of functional connectivity metrics. In addition, ICA-AROMA preserves the temporal non-artifactual time-series characteristics and limits the loss in tDoF, thereby increasing statistical power at both the subject- and the between-subject analysis level.
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              Obesity Pathogenesis: An Endocrine Society Scientific Statement

              Obesity is among the most common and costly chronic disorders worldwide. Estimates suggest that in the United States obesity affects one-third of adults, accounts for up to one-third of total mortality, is concentrated among lower income groups, and increasingly affects children as well as adults. A lack of effective options for long-term weight reduction magnifies the enormity of this problem; individuals who successfully complete behavioral and dietary weight-loss programs eventually regain most of the lost weight. We included evidence from basic science, clinical, and epidemiological literature to assess current knowledge regarding mechanisms underlying excess body-fat accumulation, the biological defense of excess fat mass, and the tendency for lost weight to be regained. A major area of emphasis is the science of energy homeostasis, the biological process that maintains weight stability by actively matching energy intake to energy expenditure over time. Growing evidence suggests that obesity is a disorder of the energy homeostasis system, rather than simply arising from the passive accumulation of excess weight. We need to elucidate the mechanisms underlying this “upward setting” or “resetting” of the defended level of body-fat mass, whether inherited or acquired. The ongoing study of how genetic, developmental, and environmental forces affect the energy homeostasis system will help us better understand these mechanisms and are therefore a major focus of this statement. The scientific goal is to elucidate obesity pathogenesis so as to better inform treatment, public policy, advocacy, and awareness of obesity in ways that ultimately diminish its public health and economic consequences. This Scientific Statement focuses on factors for which compelling evidence exists that implicates them in the pathogenesis of either the accumulation or maintenance of excess body fat mass.
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                Author and article information

                Journal
                The American Journal of Clinical Nutrition
                Oxford University Press (OUP)
                0002-9165
                1938-3207
                March 2019
                March 01 2019
                March 05 2019
                March 2019
                March 01 2019
                March 05 2019
                : 109
                : 3
                : 491-503
                Affiliations
                [1 ]UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, NL
                [2 ]Division of Human Nutrition and Health, Wageningen University & Research, Wageningen, The Netherlands
                [3 ]Montreal Neurological Institute, McGill University, Montreal, Canada
                [4 ]Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland
                [5 ]Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, German Center for Diabetes Research, Tübingen, Germany
                [6 ]Amsterdam School of Communication Research, University of Amsterdam, Amsterdam, The Netherlands
                [7 ]Department of Psychology, Stanford University, Stanford, CA
                [8 ]Department of Psychiatry, Yale School of Medicine, New Haven, CT
                [9 ]Oregon Research Institute, Eugene, OR
                Article
                10.1093/ajcn/nqy344
                30834431
                16596cd7-33a5-4c98-ae89-ef27e9f65095
                © 2019

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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