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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Gemma C. Monte-Rubio; carles falcon; Edith Pomarol-Clotet;

    There is a widespread interest in applying pattern recognition methods to anatomical neuroimaging data, but so far, there has been relatively little investigation into how best to derive image features in order to make the most accurate predictions. In this work, a Gaussian Process machine learning approach was used for predicting age, gender and body mass index (BMI) of subjects in the IXI dataset, as well as age, gender and diagnostic status using the ABIDE and COBRE datasets. MRI data were segmented and aligned using SPM12, and a variety of feature representations were derived from this preprocessing. We compared classification and regression accuracy using the different sorts of features, and with various degrees of spatial smoothing. Results suggested that feature sets that did not ignore the implicit background tissue class, tended to result in better overall performance, whereas some of the most commonly used feature sets performed relatively poorly.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ NeuroImage; OpenAPC ...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    NeuroImage; OpenAPC Global Initiative
    Article . Conference object . 2018 . Peer-reviewed
    License: CC BY
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ NeuroImage; OpenAPC ...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      NeuroImage; OpenAPC Global Initiative
      Article . Conference object . 2018 . Peer-reviewed
      License: CC BY
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Caterina Gratton; Ally Dworetsky; Rebecca S. Coalson; Babatunde Adeyemo; +14 Authors

    AbstractDenoising fMRI data requires assessment of frame-to-frame head motion and removal of the biases motion introduces. This is usually done through analysis of the parameters calculated during retrospective head motion correction (i.e., ‘motion’ parameters). However, it is increasingly recognized that respiration introduces factitious head motion via perturbations of the main (B0) field. This effect appears as higher-frequency fluctuations in the motion parameters (> 0.1 Hz, here referred to as ‘HF-motion’), primarily in the phase-encoding direction. This periodicity can sometimes be obscured in standard single-band fMRI (TR 2.0 – 2.5 s.) due to aliasing. Here we examined (1) how prevalent HF-motion effects are in seven single-band datasets with TR from 2.0 - 2.5 s and (2) how HF-motion affects functional connectivity. We demonstrate that HF-motion is relatively trait-like and more common in older adults, those with higher body mass index, and those with lower cardiorespiratory fitness. We propose a low-pass filtering approach to remove the contamination of high frequency effects from motion summary measures, such as framewise displacement (FD). We demonstrate that in most datasets this filtering approach saves a substantial amount of data from FD-based frame censoring, while at the same time reducing motion biases in functional connectivity measures. These findings suggest that filtering motion parameters is an effective way to improve the fidelity of head motion estimates, even in single band datasets. Particularly large data savings may accrue in datasets acquired in older and less fit participants.Highlights-Single-band fMRI motion traces show factitious high-frequency content (HF-motion)-The magnitude of HF-motion relates to age and other demographic factors-HF-motion elevates framewise displacement (FD) and causes data loss-Substantial fMRI data can be recovered from censoring by filtering motion traces-Filtering motion traces reduces motion artifacts in functional connectivity

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ bioRxivarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    bioRxiv
    Preprint . 2019
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Europe PubMed Central
    Other literature type . 2020
    Data sources: PubMed Central
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    NeuroImage
    Article . 2020
    Data sources: DOAJ-Articles
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    NeuroImage
    Article . Preprint . 2020 . Peer-reviewed
    License: CC BY NC ND
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ bioRxivarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      bioRxiv
      Preprint . 2019
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Europe PubMed Central
      Other literature type . 2020
      Data sources: PubMed Central
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      NeuroImage
      Article . 2020
      Data sources: DOAJ-Articles
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      NeuroImage
      Article . Preprint . 2020 . Peer-reviewed
      License: CC BY NC ND
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Federica Prinelli; Laura Fratiglioni; Grégoria Kalpouzos; Massimo Musicco; +4 Authors

    Abstract Optimal nutrition may play a beneficial role in maintaining a healthy brain. However, the relationship between nutrient intake and brain integrity is largely unknown. We investigated the association of specific nutrient dietary patterns with structural characteristics of the brain. Within the population-based Swedish National study on Aging and Care-Kungsholmen (SNAC-K), a cross-sectional study of 417 dementia-free participants aged ≥60 years who underwent structural magnetic resonance imaging (MRI) scans during 2001–2003, was carried-out. Data on dietary intake were collected using a food frequency questionnaire, from which intake of 21 nutrients was estimated. By principal component analysis, five nutrient patterns were extracted: (1) NP1 was characterized by fiber, vitamin C, E, β-carotene, and folate [Fiber&Antioxidants], (2) NP2 by eicosapentaenoic (EPA, 20:5 ω-3) and docosahexaenoic (DHA, 22:6 ω-3) polyunsaturated fatty acids (PUFAs), proteins, cholesterol, vitamin B3, B12, and D [long chain (LC) ω-3PUFAs&Proteins], (3) NP3 by α-linoleic (18:2 ω-6) and α-linolenic (18:3 ω-3) PUFAs, monounsaturated fatty acids (MUFAs), and vitamin E [MUFAs&ω-3,6PUFAs], (4) NP4 by saturated fatty acids (SFAs), trans fats, MUFAs, and cholesterol [SFAs&Trans fats], (5) NP5 by B-vitamins, retinol, and proteins [B-Vitamins&Retinol]. Nutrient patterns scores were tertiled with the lowest tertile as reference, and were related to total brain volume (TBV) and white matter hyperintensities volume (WMHV) using linear regression models adjusting for potential confounders. In the multi-adjusted model, compared to the lowest intake for each pattern, the highest intake of NP1 (β = 11.11, P = 0.009), NP2 (β = 7.47, P = 0.052), and NP3 (β = 10.54, P = 0.005) was associated with larger TBV whereas NP5 was related to smaller TBV (β = −12.82, P = 0.001). The highest intake of NP1 was associated with lower WMHV (β = −0.32, P = 0.049), whereas NP4 was associated with greater WMHV (β = 0.31, P = 0.036). In sum, our results suggest that the identified brain-health specific nutrient combinations characterized by higher intake of fruit, vegetables, legumes, olive and seed oils, fish, lean red meat, poultry and low in milk and dairy products, cream, butter, processed meat and offal, were strongly associated with greater brain integrity among older adults.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ NeuroImagearrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    NeuroImage
    Article . 2019
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    NeuroImage
    Other literature type . Article . 2019 . Peer-reviewed
    License: Elsevier TDM
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    CNR ExploRA
    Article . 2019
    Data sources: CNR ExploRA
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ NeuroImagearrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      NeuroImage
      Article . 2019
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      NeuroImage
      Other literature type . Article . 2019 . Peer-reviewed
      License: Elsevier TDM
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      CNR ExploRA
      Article . 2019
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Ulrike Toepel; Kathrin Ohla; Julie Hudry; Johannes le Coutre; +1 Authors

    Abstract The influence of external factors on food preferences and choices is poorly understood. Knowing which and how food-external cues impact the sensory processing and cognitive valuation of food would provide a strong benefit toward a more integrative understanding of food intake behavior and potential means of interfering with deviant eating patterns to avoid detrimental health consequences for individuals in the long run. We investigated whether written labels with positive and negative (as opposed to ‘neutral’) valence differentially modulate the spatio-temporal brain dynamics in response to the subsequent viewing of high- and low-energetic food images. Electrical neuroimaging analyses were applied to visual evoked potentials (VEPs) from 20 normal-weight participants. VEPs and source estimations in response to high- and low- energy foods were differentially affected by the valence of preceding word labels over the ~ 260–300 ms post-stimulus period. These effects were only observed when high-energy foods were preceded by labels with positive valence. Neural sources in occipital as well as posterior, frontal, insular and cingulate regions were down-regulated. These findings favor cognitive–affective influences especially on the visual responses to high-energetic food cues, potentially indicating decreases in cognitive control and goal-adaptive behavior. Inverse correlations between insular activity and effectiveness in food classification further indicate that this down-regulation directly impacts food-related behavior.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ NeuroImagearrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    NeuroImage
    Article . 2013
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Article . 2013
    License: CC BY NC ND
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    NeuroImage
    Article . 2014 . Peer-reviewed
    License: Elsevier TDM
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      NeuroImage
      Article . 2013
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      ZENODO
      Article . 2013
      License: CC BY NC ND
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      NeuroImage
      Article . 2014 . Peer-reviewed
      License: Elsevier TDM
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Klaus Scheffler; Martin Heni; Hubert Preißl; Martina F Callaghan; +2 Authors

    Obesity-related structural brain alterations point to a consistent reduction in gray matter with increasing body mass index (BMI) but changes in white matter have proven to be more complex and less conclusive. Hence, more recently diffusion tensor imaging (DTI) has been employed to investigate microstructural changes in white matter structure. Altogether, these studies have mostly shown a loss of white matter integrity with obesity-related factors in several brain regions. However, the variety of these obesity-related factors, including inflammation and dyslipidemia, resulted in competing influences on the DTI indices. To increase the specificity of DTI results, we explored specific brain tissue properties by combining DTI with quantitative multi-parameter mapping in lean, overweight and obese young adults. By means of multi-parameter mapping, white matter structures showed differences in MRI parameters consistent with reduced myelin, increased water and altered iron content with increasing BMI in the superior longitudinal fasciculus, anterior thalamic radiation, internal capsule and corpus callosum. BMI-related changes in DTI parameters revealed mainly alterations in mean and axial diffusivity with increasing BMI in the corticospinal tract, anterior thalamic radiation and superior longitudinal fasciculus. These alterations, including mainly fiber tracts linking limbic structures with prefrontal regions, could potentially promote accelerated aging in obese individuals leading to an increased risk for cognitive decline.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ NeuroImage; OpenAPC ...arrow_drop_down
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    NeuroImage; OpenAPC Global Initiative
    Article . Conference object . 2016 . Peer-reviewed
    License: CC BY
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      NeuroImage; OpenAPC Global Initiative
      Article . Conference object . 2016 . Peer-reviewed
      License: CC BY
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Binnewies, Julia; Nawijn, Laura; van Tol, Marie-José; van der Wee, Nic J.A.; +2 Authors

    Background: Depression has been associated with decreased regional grey matter volume, which might partly be explained by an unhealthier lifestyle in depressed individuals which has been ignored by most earlier studies. Also, the longitudinal nature of depression, lifestyle and brain structure associations is largely unknown. This study investigates the relationship of depression and lifestyle with brain structure cross-sectionally and longitudinally over up to 9 years. Methods: We used longitudinal structural MRI data of persons with depression and/or anxiety disorders and controls (Nunique participants = 347, Nobservations = 609). Cortical thickness of medial orbitofrontal cortex (mOFC), rostral anterior cingulate cortex (rACC) and hippocampal volume were derived using FreeSurfer. Using Generalized Estimating Equations, we investigated associations of depression and lifestyle (Body mass index (BMI), smoking, alcohol consumption, physical activity and sleep duration) with brain structure and change in brain structure over 2 (n = 179) and 9 years (n = 82). Results: Depression status (B = -.053, p = .002) and severity (B = -.002, p = .002) were negatively associated with rACC thickness. mOFC thickness was negatively associated with BMI (B = -.004, p < .001) and positively with moderate alcohol consumption (B = .030, p = .009). All associations were independent of each other. No associations were observed between (change in) depression, disease burden or lifestyle factors with brain change over time. Conclusions: Depressive symptoms and diagnosis were independently associated with thinner rACC, BMI with thinner mOFC, and moderate alcohol consumption with thicker mOFC. No longitudinal associations were observed, suggesting that regional grey matter alterations are a long-term consequence or vulnerability indicator for depression but not dynamically or progressively related to depression course trajectory.

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    NARCIS
    Article . 2021
    Data sources: NARCIS
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    NeuroImage
    Article . 2021
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Article . 2021
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    Article . 2020
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    Article . 2021
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    NeuroImage
    Other literature type . Article . 2021 . Peer-reviewed
    License: Elsevier TDM
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      Article . 2021
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Article . 2021
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      NeuroImage
      Article . 2020
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      NeuroImage
      Article . 2021
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      NeuroImage
      Other literature type . Article . 2021 . Peer-reviewed
      License: Elsevier TDM
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Franke, Katja; Gaser, Christian; de Rooij, Susanne R; Schwab, Matthias; +1 Authors

    BACKGROUND: Prenatal exposure to undernutrition is widespread in both developing and industrialized countries, causing irreversible damage to the developing brain, resulting in altered brain structure and decreased cognitive function during adulthood. The Dutch famine in 1944/45 was a humanitarian disaster, now enabling studies of the effects of prenatal undernutrition during gestation on brain aging in late adulthood.METHODS: We hypothesized that study participants prenatally exposed to maternal nutrient restriction (MNR) would demonstrate altered brain structure resembling premature brain aging in late adulthood, expecting the effect being stronger in men. Utilizing the Dutch famine birth cohort (n = 118; mean age: 67.5 ± 0.9 years), this study implements an innovative biomarker for individual brain aging, using structural neuroimaging. BrainAGE was calculated using state-of-the-art pattern recognition methods, trained on an independent healthy reference sample, then applied to the Dutch famine MRI sample, to evaluate the effects of prenatal undernutrition during early gestation on individual brain aging in late adulthood.RESULTS: Exposure to famine in early gestation was associated with BrainAGE scores indicative of an older-appearing brain in the male sample (mean difference to subjects born before famine: 4.3 years, p < 0.05). Furthermore, in explaining the observed variance in individual BrainAGE scores in the male sample, maternal age at birth, head circumference at birth, medical treatment of hypertension, history of cerebral incidences, actual heart rate, and current alcohol intake emerged to be the most influential variables (adjusted R2 = 0.63, p < 0.01).INTERPRETATION: The findings of our study on exposure to prenatal undernutrition being associated with a status of premature brain aging during late adulthood, as well as individual brain structure being shaped by birth- and late-life health characteristics, are strongly supporting the critical importance of sufficient nutrient supply during pregnancy. Interestingly, the status of premature brain aging in participants exposed to the Dutch famine during early gestation occurred in the absence of fetal growth restriction at birth as well as vascular pathology in late-life. Additionally, the neuroimaging brain aging biomarker presented in this study will further enable tracking effects of environmental influences or (preventive) treatments on individual brain maturation and aging in epidemiological and clinical studies.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ NeuroImagearrow_drop_down
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    NeuroImage
    Article . 2017
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    NeuroImage
    Article . 2018
    Data sources: NARCIS
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    NeuroImage
    Article . 2018 . Peer-reviewed
    License: Elsevier TDM
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    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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    Article . 2018
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      Article . 2017
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      NeuroImage
      Article . 2018
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      NeuroImage
      Article . 2018 . Peer-reviewed
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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      Article . 2018
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Marc Baroncini; Patrice Jissendi; Eglantine Balland; Pierre Besson; +5 Authors

    International audience; Gaining new insights into the anatomy of the human hypothalamus is crucial for the development of new treatment strategies involving functional stereotactic neurosurgery. Here, using anatomical comparisons between histology and magnetic resonance images of the human hypothalamus in the coronal plane, we show that discrete gray and white hypothalamic structures are consistently identifiable by MRI. Macroscopic and microscopic images were used to precisely annotate the MRI sequences realized in the coronal plane in twenty healthy volunteers. MRI was performed on a 1.5 T scanner, using a protocol including T1-weighted 3D fast field echo, T1-weighted inversion-recovery, turbo spin echo and T2-weighted 2D fast field echo imaging. For each gray matter structure as well as for white matter bundles, the different MRI sequences were analyzed in comparison to each other. The anterior commissure and the fornix were often identifiable, while the mammillothalamic tract was more difficult to spot. Qualitative analyses showed that MRI could also highlight finer structures such as the paraventricular nucleus, the ventromedial nucleus of the hypothalamus and the infundibular (arcuate) nucleus, brain nuclei that play key roles in the regulation of food intake and energy homeostasis. The posterior hypothalamic area, a target for deep brain stimulation in the treatment of cluster headaches, was readily identified, as was the lateral hypothalamic area, which similar to the aforementioned hypothalamic nuclei, could be a putative target for deep brain stimulation in the treatment of obesity. Finally, each of the identified structures was mapped to Montreal Neurological Institute (MNI) space.

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    Other literature type . 2012
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    NeuroImage
    Article . 2010
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    NeuroImage
    Article . 2012 . Peer-reviewed
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      Other literature type . 2012
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      NeuroImage
      Article . 2010
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      NeuroImage
      Article . 2012 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Gemma C. Monte-Rubio; carles falcon; Edith Pomarol-Clotet;

    There is a widespread interest in applying pattern recognition methods to anatomical neuroimaging data, but so far, there has been relatively little investigation into how best to derive image features in order to make the most accurate predictions. In this work, a Gaussian Process machine learning approach was used for predicting age, gender and body mass index (BMI) of subjects in the IXI dataset, as well as age, gender and diagnostic status using the ABIDE and COBRE datasets. MRI data were segmented and aligned using SPM12, and a variety of feature representations were derived from this preprocessing. We compared classification and regression accuracy using the different sorts of features, and with various degrees of spatial smoothing. Results suggested that feature sets that did not ignore the implicit background tissue class, tended to result in better overall performance, whereas some of the most commonly used feature sets performed relatively poorly.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ NeuroImage; OpenAPC ...arrow_drop_down
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    NeuroImage; OpenAPC Global Initiative
    Article . Conference object . 2018 . Peer-reviewed
    License: CC BY
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      NeuroImage; OpenAPC Global Initiative
      Article . Conference object . 2018 . Peer-reviewed
      License: CC BY
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    Authors: Caterina Gratton; Ally Dworetsky; Rebecca S. Coalson; Babatunde Adeyemo; +14 Authors

    AbstractDenoising fMRI data requires assessment of frame-to-frame head motion and removal of the biases motion introduces. This is usually done through analysis of the parameters calculated during retrospective head motion correction (i.e., ‘motion’ parameters). However, it is increasingly recognized that respiration introduces factitious head motion via perturbations of the main (B0) field. This effect appears as higher-frequency fluctuations in the motion parameters (> 0.1 Hz, here referred to as ‘HF-motion’), primarily in the phase-encoding direction. This periodicity can sometimes be obscured in standard single-band fMRI (TR 2.0 – 2.5 s.) due to aliasing. Here we examined (1) how prevalent HF-motion effects are in seven single-band datasets with TR from 2.0 - 2.5 s and (2) how HF-motion affects functional connectivity. We demonstrate that HF-motion is relatively trait-like and more common in older adults, those with higher body mass index, and those with lower cardiorespiratory fitness. We propose a low-pass filtering approach to remove the contamination of high frequency effects from motion summary measures, such as framewise displacement (FD). We demonstrate that in most datasets this filtering approach saves a substantial amount of data from FD-based frame censoring, while at the same time reducing motion biases in functional connectivity measures. These findings suggest that filtering motion parameters is an effective way to improve the fidelity of head motion estimates, even in single band datasets. Particularly large data savings may accrue in datasets acquired in older and less fit participants.Highlights-Single-band fMRI motion traces show factitious high-frequency content (HF-motion)-The magnitude of HF-motion relates to age and other demographic factors-HF-motion elevates framewise displacement (FD) and causes data loss-Substantial fMRI data can be recovered from censoring by filtering motion traces-Filtering motion traces reduces motion artifacts in functional connectivity

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ bioRxivarrow_drop_down
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    bioRxiv
    Preprint . 2019
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    Europe PubMed Central
    Other literature type . 2020
    Data sources: PubMed Central
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    NeuroImage
    Article . 2020
    Data sources: DOAJ-Articles
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    NeuroImage
    Article . Preprint . 2020 . Peer-reviewed
    License: CC BY NC ND
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      bioRxiv
      Preprint . 2019
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      Europe PubMed Central
      Other literature type . 2020
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      NeuroImage
      Article . 2020
      Data sources: DOAJ-Articles
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      NeuroImage
      Article . Preprint . 2020 . Peer-reviewed
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    Authors: Federica Prinelli; Laura Fratiglioni; Grégoria Kalpouzos; Massimo Musicco; +4 Authors

    Abstract Optimal nutrition may play a beneficial role in maintaining a healthy brain. However, the relationship between nutrient intake and brain integrity is largely unknown. We investigated the association of specific nutrient dietary patterns with structural characteristics of the brain. Within the population-based Swedish National study on Aging and Care-Kungsholmen (SNAC-K), a cross-sectional study of 417 dementia-free participants aged ≥60 years who underwent structural magnetic resonance imaging (MRI) scans during 2001–2003, was carried-out. Data on dietary intake were collected using a food frequency questionnaire, from which intake of 21 nutrients was estimated. By principal component analysis, five nutrient patterns were extracted: (1) NP1 was characterized by fiber, vitamin C, E, β-carotene, and folate [Fiber&Antioxidants], (2) NP2 by eicosapentaenoic (EPA, 20:5 ω-3) and docosahexaenoic (DHA, 22:6 ω-3) polyunsaturated fatty acids (PUFAs), proteins, cholesterol, vitamin B3, B12, and D [long chain (LC) ω-3PUFAs&Proteins], (3) NP3 by α-linoleic (18:2 ω-6) and α-linolenic (18:3 ω-3) PUFAs, monounsaturated fatty acids (MUFAs), and vitamin E [MUFAs&ω-3,6PUFAs], (4) NP4 by saturated fatty acids (SFAs), trans fats, MUFAs, and cholesterol [SFAs&Trans fats], (5) NP5 by B-vitamins, retinol, and proteins [B-Vitamins&Retinol]. Nutrient patterns scores were tertiled with the lowest tertile as reference, and were related to total brain volume (TBV) and white matter hyperintensities volume (WMHV) using linear regression models adjusting for potential confounders. In the multi-adjusted model, compared to the lowest intake for each pattern, the highest intake of NP1 (β = 11.11, P = 0.009), NP2 (β = 7.47, P = 0.052), and NP3 (β = 10.54, P = 0.005) was associated with larger TBV whereas NP5 was related to smaller TBV (β = −12.82, P = 0.001). The highest intake of NP1 was associated with lower WMHV (β = −0.32, P = 0.049), whereas NP4 was associated with greater WMHV (β = 0.31, P = 0.036). In sum, our results suggest that the identified brain-health specific nutrient combinations characterized by higher intake of fruit, vegetables, legumes, olive and seed oils, fish, lean red meat, poultry and low in milk and dairy products, cream, butter, processed meat and offal, were strongly associated with greater brain integrity among older adults.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ NeuroImagearrow_drop_down
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    NeuroImage
    Article . 2019
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    NeuroImage
    Other literature type . Article . 2019 . Peer-reviewed
    License: Elsevier TDM
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    CNR ExploRA
    Article . 2019
    Data sources: CNR ExploRA
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      NeuroImage
      Article . 2019
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      NeuroImage
      Other literature type . Article . 2019 . Peer-reviewed
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      CNR ExploRA
      Article . 2019
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    Authors: Ulrike Toepel; Kathrin Ohla; Julie Hudry; Johannes le Coutre; +1 Authors

    Abstract The influence of external factors on food preferences and choices is poorly understood. Knowing which and how food-external cues impact the sensory processing and cognitive valuation of food would provide a strong benefit toward a more integrative understanding of food intake behavior and potential means of interfering with deviant eating patterns to avoid detrimental health consequences for individuals in the long run. We investigated whether written labels with positive and negative (as opposed to ‘neutral’) valence differentially modulate the spatio-temporal brain dynamics in response to the subsequent viewing of high- and low-energetic food images. Electrical neuroimaging analyses were applied to visual evoked potentials (VEPs) from 20 normal-weight participants. VEPs and source estimations in response to high- and low- energy foods were differentially affected by the valence of preceding word labels over the ~ 260–300 ms post-stimulus period. These effects were only observed when high-energy foods were preceded by labels with positive valence. Neural sources in occipital as well as posterior, frontal, insular and cingulate regions were down-regulated. These findings favor cognitive–affective influences especially on the visual responses to high-energetic food cues, potentially indicating decreases in cognitive control and goal-adaptive behavior. Inverse correlations between insular activity and effectiveness in food classification further indicate that this down-regulation directly impacts food-related behavior.

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    NeuroImage
    Article . 2013
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    ZENODO
    Article . 2013
    License: CC BY NC ND
    Data sources: ZENODO
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    NeuroImage
    Article . 2014 . Peer-reviewed
    License: Elsevier TDM
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      NeuroImage
      Article . 2013
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      ZENODO
      Article . 2013
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      NeuroImage
      Article . 2014 . Peer-reviewed
      License: Elsevier TDM
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