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758 Research products (1 rule applied)

  • Neuroinformatics
  • 2019-2023

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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: Noemi Passarello; Francesca Greco; Fabrizio Piras; Barbara Cordella; +4 Authors

    Contemporary neurosciences have shown that emotion, thought and language involve the functioning of connected brain areas, which allow the recognition and expression of one���s own feelings. The present pilot study was aimed at investigating the link among verbal expression of emotional experiences (assessed through the Toronto Structured Interview for Alexithymia - TSIA), the capacity of emotional regulation (assessed by means of the Attachment Style Questionnaire, the Emotion Regulation Questionnaire, and the Coping Orientation to Problems Experiences), the linguistic structure and symbolic representation of narration (assessed through the Text Mining Analyses), and brain area structures (by means of a 3-T high-resolution structural Magnetic Resonance Imaging). To this aim, nine (males=5) healthy adult subjects were interviewed by means of the TSIA and scored on psychological questionnaires. The cortical and subcortical structural measures were also evaluated. TSIA transcripts were then analysed by using a cluster analysis and, subsequently, a correspondence analysis, and the values of lexical factors were correlated with cortical and subcortical structural measures as well as with TSIA and psychological scores. Overall, the reciprocal relationships among the measures of neurobiological structures, the capacity of identifying, describing and regulating emotions, and the ability of translating the emotional experiences into verbal forms, allowed to identify a complex pattern of biopsychosocial aspects, getting light on referential processes characterizing the verbally and not-verbally express emotions. Psychology Hub, Vol. 38 No. 2 (2021)

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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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  • Authors: A Pascarella and F Pitolli;

    Understanding brain function from magneto-electroencephalographic (M/EEG) measurements requires advanced mathematical and signal processing tools. Although the analysis of M/EEG data at sensors level sheds light on important brain mechanisms, full exploitation of the information contained in such brain data could be achieved by reconstructing the active neural sources from M/EEG measurements. This involves solving an ill-posed and ill-conditioned inverse problem in which not only the identification of the most suitable inversion method [1, 2] but also the calibration of the regularization parameters is of paramount importance. Once time series representing brain activity are available, a next step is to develop tools to extract meaningful information that characterizes brain activity [3, 4], for example, when the subject under study is affected by diseases that impair brain function. The mini-symposium brings together researchers from various disciplines who have developed methodologies that are being successfully used for the analysis of the M/EEG data, the solution of the underlying inverse problem and in the definition of brain fingerprint. The purpose is not only to present the latest research results in this area but also to create a fruitful environment for the development of new ideas.

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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: Brébion G; Núñez C; Lombardini F; Senior C; +4 Authors

    BACKGROUND: Depressive symptoms are known to affect memory efficiency in various populations. More specifically, several studies conducted in patients suffering from schizophrenia have indicated that memory efficiency is affected by depressed mood in female patients and by anxiety in male patients. We investigated, using neuroimaging techniques, whether similar gender-specific associations with subclinical depression and anxiety could be observed in a non-clinical sample. METHOD: Forty-five healthy Spanish-speaking individuals (23 females) were administered a verbal memory task. Lists of high- and low-frequency words were presented. Immediate free recall was requested after the learning of each list, and a yes/no recognition task was completed during the acquisition of the fMRI data. RESULTS: Regression analyses revealed that higher depression scores in women, and higher anxiety scores in men, were associated with poorer recall. In women, higher depression scores were further associated with decreased cerebral activity in the right temporoparietal junction, left inferior occipitotemporal gyrus, bilateral thalamus, and left anterior cingulate during correct recognition of target words. In men, anxiety scores were not associated with any cerebral activity. CONCLUSIONS: Subclinical depression in women appears to affect memory efficiency by impacting cerebral regions specifically recruited for the cognitive demands of the task, as well as cerebral regions more generally involved in arousal, decision-making, and emotional regulation. Anxiety in men might impact the encoding memory processes. The results, although preliminary, suggest that gender differences may need to be taken into account when developing strategies for the cognitive and pharmacological remediation of memory impairment.

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    Authors: Shokouhi, S;

    imaging and cognitive data from 86 subjects: ws2-based tau measure (tau), global amyloid PET SUVR (abeta), regional amyloid PET SUVR in temporal lobe (temp), cingulate cortex, parietal lobe (par), frontal lobe(front) , tau PET SUVRs (ibraak1, ibraak34, ibraak56), standard objective cognitive performance (mmse, adas, adef, admem), and subjective cognitive scores (emem,elan, eviso, eplan, eorg, ediv) THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    Dataset . 2020
    Data sources: B2FIND
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    Mendeley Data; NARCIS
    Dataset . 2020
    License: CC BY NC
    Data sources: Datacite; NARCIS
    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
    Mendeley Data
    Dataset . 2020
    License: CC BY NC
    Data sources: Mendeley Data
    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
    Mendeley Data
    Dataset . 2020
    License: CC BY NC
    Data sources: Mendeley Data
    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
    Mendeley Data
    Dataset . 2020
    License: CC BY NC
    Data sources: Datacite
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      Dataset . 2020
      Data sources: B2FIND
      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/
      Mendeley Data; NARCIS
      Dataset . 2020
      License: CC BY NC
      Data sources: Datacite; NARCIS
      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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      Dataset . 2020
      License: CC BY NC
      Data sources: Mendeley Data
      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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      Dataset . 2020
      License: CC BY NC
      Data sources: Mendeley Data
      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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      Dataset . 2020
      License: CC BY NC
      Data sources: Datacite
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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: Xuezhu Cai;

    This dataset contains raw MRI NIFTI images acquired for the published paper titled "Mild Repetitive Head Impacts Alter Perivascular Flow in the Midbrain Dopaminergic System in Awake Rats and Increase Microgliosis and AQP4 Expression and Depolarization". The dataset has a control group and a mild head impact group indicated by the folder name. Each subfolder has both T1-weighted axial and sagittal MRI images acquired using Fast Low Angle Shot (FLASH) sequence (TR/TE = 300/2.5ms and Flip Angle = 30°) and images were collected every 2 min and 41 sec for two hours after contrast administration. A contrast agent phantom was attached to the head coil during each scan for validation of image intensity rescaling.

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    Mendeley Data; NARCIS
    Dataset . 2021
    License: CC BY
    Data sources: Datacite; NARCIS
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    Mendeley Data
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    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/
    Mendeley Data
    Dataset . 2021
    License: CC BY
    Data sources: Mendeley Data
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    Mendeley Data
    Dataset . 2021
    License: CC BY
    Data sources: Mendeley Data
    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/
    DANS-EASY
    Dataset . 2021
    Data sources: B2FIND
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      Mendeley Data; NARCIS
      Dataset . 2021
      License: CC BY
      Data sources: Datacite; NARCIS
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      Mendeley Data
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      Dataset . 2021
      License: CC BY
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      Dataset . 2021
      License: CC BY
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      Dataset . 2021
      Data sources: B2FIND
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    The aim of this study was to characterize the magnetization transfer (MT) effects in locus coeruleus (LC) imaging by applying high spatial resolution quantitative MT (qMT) imaging to create parametric maps of the macromolecular content of the LC and surrounding tissues. Healthy volunteers (n=26; sex=17F/9M; age range=20-72 years) underwent brain MRI on a 3.0 Tesla scanner. qMT data were acquired using a 3D MT-prepared spoiled gradient echo sequence. A traditional Neuromelanin (NM) scan consisting of a T1-weighted turbo spin echo sequence with MT preparation was also acquired. PSR was estimated for each voxel using a single-point qMT approach (Yarnykh VL, 2012, doi: 10.1002/mrm.23224). This repository includes the qMT datasets for the 26 participants. The dataset for each subject includes the following preprocessed images: - B0map.nii - B1map.nii - NM_2_qMT.nii (NM scan using 2D multislice TSE) - qMT_1pt_mc.nii (motion corrected qMT data) - qMT_2kHz.nii (MT-weighted image) - qMT_ref.nii (Reference image - No MT) - T1mfa.nii (multiple flip angle images to calculate the T1 map) All the above are coregistered to qMT_2kHz.nii. The dataset also includes the following processed images: - PSR_kba12p5.nii (PSR map) - T1obs.nii (T1 map) Matlab code required for data analysis has been posted at https://github.com/smithalexk/pulsed_qMT/tree/5ec63095c3b4e426a076b5730bffcab415df9c95 When using the code, please cite - Smith AK, et al., Neuroimage, 2014; doi: 10.1016/j.neuroimage.2014.03.005 - Trujillo P, et al., Neuroradiology, 2017; doi: 10.1007/s00234-017-1911-2

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    Mendeley Data
    Dataset . 2019
    License: CC BY
    Data sources: Mendeley Data
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    Mendeley Data; NARCIS
    Dataset . 2019
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    Mendeley Data
    Dataset . 2019
    License: CC BY
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    DANS-EASY
    Dataset . 2019
    Data sources: B2FIND
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    DANS-EASY
    Dataset . 2019
    Data sources: B2FIND
    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/
    Mendeley Data
    Dataset . 2019
    License: CC BY
    Data sources: Mendeley Data
    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/
    DANS-EASY
    Dataset . 2019
    Data sources: B2FIND
    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/
    Mendeley Data
    Dataset . 2019
    License: CC BY
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    Mendeley Data
    Dataset . 2019
    License: CC BY
    Data sources: Mendeley Data
    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/
    Mendeley Data; NARCIS
    Dataset . 2019
    License: CC BY
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    Mendeley Data; NARCIS
    Dataset . 2019
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      Dataset . 2019
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      Dataset . 2019
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      Dataset . 2019
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      Dataset . 2019
      Data sources: B2FIND
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      Dataset . 2019
      Data sources: B2FIND
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      Dataset . 2019
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      Dataset . 2019
      Data sources: B2FIND
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      Dataset . 2019
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      Dataset . 2019
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      Mendeley Data; NARCIS
      Dataset . 2019
      License: CC BY
      Data sources: Datacite; 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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    Be sure to download the most recent version to gurantee accuracy. This dataset contains normalized MRI data of 74 GBM patients from the Cancer Imaging Archive (including T1-weighted, T2-weighted, T2-weighted FLAIR and T1-weighted gadolinium contrast-enhanced. Each patient with a TCGA number.) and corresponding segmented tumor areas (the whole tumor area and the solid tumor core). This data set was mentioned in "Biological pathways underlying prognostic radiomics phenotypes from paired MRI and RNA sequencing in glioblastoma", which has been published by Radiology at https://doi.org/10.1148/radiol.2021203281.

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    Mendeley Data
    Dataset . 2020
    License: CC BY
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    DANS-EASY
    Dataset . 2021
    Data sources: B2FIND
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    Mendeley Data; NARCIS
    Dataset . 2021
    License: CC BY
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    DANS-EASY
    Dataset . 2020
    Data sources: B2FIND
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    Dataset . 2020
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      Dataset . 2021
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      Dataset . 2021
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      Dataset . 2020
      Data sources: B2FIND
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      Dataset . 2020
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      Dataset . 2020
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    raw data THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    Mendeley Data; NARCIS
    Dataset . 2021
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    Mendeley Data; NARCIS
    Dataset . 2021
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    Dataset . 2021
    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/
    DANS-EASY
    Dataset . 2021
    Data sources: B2FIND
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    DANS-EASY
    Dataset . 2021
    Data sources: B2FIND
    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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      Dataset . 2021
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      Dataset . 2021
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      Dataset . 2021
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      Dataset . 2021
      Data sources: B2FIND
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      Dataset . 2021
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      Dataset . 2021
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      Dataset . 2021
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    Authors: Nishida, Daisuke;

    Data sets of gait performance with 10-meter walk test (10MWT) and brain activation data of functional magnetic resonance imaging.

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    Mendeley Data
    Dataset . 2021
    License: CC BY
    Data sources: Mendeley Data
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    Mendeley Data
    Dataset . 2021
    License: CC BY
    Data sources: Mendeley Data
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    Mendeley Data
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    Mendeley Data
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      Mendeley Data
      Dataset . 2021
      License: CC BY
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      Mendeley Data
      Dataset . 2021
      License: CC BY
      Data sources: Mendeley Data
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      Mendeley Data
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      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/
      Mendeley Data
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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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: Boswinkel, Vivian;

    This thesis gives insights into neonatal brain injury, brain growth and neurodevelopmental outcome in moderate-late preterm (MLPT) infants, born at 32–36 weeks’ gestation. Additionally, we describe the neurodevelopmental outcome of preterm infants with cerebellar hemorrhage (CBH). In Chapter 2 we give an overview of the literature regarding brain injury and development in MLPT infants. Most frequently studied types of brain injury were intraventricular hemorrhage (IVH) and periventricular leukomalacia (PVL). Incidences of other forms of brain injury were sparsely reported. In addition, there is a lack of knowledge regarding the effect of brain injury on neurodevelopmental outcome in MLPT infants. In Chapter 3, we investigate current practice regarding neurological surveillance in MLPT infants in the Netherlands and Canada. We found variations in local practices regarding e.g. admission criteria, neuroimaging and follow-up. This may partially be explained by the lack of guidelines regarding care of MLPT infants. The above indicates that considerable knowledge about MLPT infants is still lacking. By conducting a prospective cohort study, we sought to fill some of the gaps in knowledge. The BIMP-study (Brain Imaging in Moderate-late Preterm infants) was performed between 2017-2019 at the Isala Women and children’s Center in Zwolle, the Netherlands. MLPT infants underwent cranial ultrasound and MRI. In the BIMP-cohort (n= 166), a high incidence of mild brain lesions (71.7%) was found. Frequent findings were signs suggestive of white matter injury, and small hemorrhages, including CBH. Moderate-severe brain lesions were seen in 3.6% of infants. We compared linear measurements of brain structures between MLPT and full-term infants. Biparietal diameter and basal ganglia width were smaller in MLPT infants. We found no differences in linear measurements or in brain volumes between moderate preterm (MP, 32+0–33+6 weeks) and late preterm infants (LP, 34+0–35+6 weeks). Mild brain lesions had no effect on brain volumes (Chapters 4, 5 and 6). In Chapter 7, we present a preliminary overview of the follow-up of 85 MLPT infants who have now reached 24 months of age. Almost one in three (30.6%) had an suboptimal outcome score. Given the descriptive nature of this overview and the fact that follow-up is still ongoing, we could not yet draw any further conclusions. In Chapter 8, we explore the association between the size and location of CBH in preterm infants ≤34 weeks (n = 218) and neurological outcome around two years of age. All infants with massive CBH and involvement of the vermis on neonatal MRI had a less favorable outcome. Infants with limited CBH had a more favorable outcome than infants with massive CBH. Although much research is still needed to provide strict recommendations, we advise the following based on our findings and the current literature: - Standard neuroimaging in MLPT infants does not seem warranted at present. - Use a low threshold for accepting MLPT infants in neurodevelopmental follow-up programs and interventions as they are at risk of neurodevelopmental and behavioral problems. - Assess CBH based on size and location. With a special note on vermian involvement.

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    NARCIS
    Doctoral thesis . 2021
    Data sources: NARCIS
    https://doi.org/10.33540/832...
    Thesis . 2021 . Peer-reviewed
    Data sources: Crossref
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      Doctoral thesis . 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/
    Authors: Noemi Passarello; Francesca Greco; Fabrizio Piras; Barbara Cordella; +4 Authors

    Contemporary neurosciences have shown that emotion, thought and language involve the functioning of connected brain areas, which allow the recognition and expression of one���s own feelings. The present pilot study was aimed at investigating the link among verbal expression of emotional experiences (assessed through the Toronto Structured Interview for Alexithymia - TSIA), the capacity of emotional regulation (assessed by means of the Attachment Style Questionnaire, the Emotion Regulation Questionnaire, and the Coping Orientation to Problems Experiences), the linguistic structure and symbolic representation of narration (assessed through the Text Mining Analyses), and brain area structures (by means of a 3-T high-resolution structural Magnetic Resonance Imaging). To this aim, nine (males=5) healthy adult subjects were interviewed by means of the TSIA and scored on psychological questionnaires. The cortical and subcortical structural measures were also evaluated. TSIA transcripts were then analysed by using a cluster analysis and, subsequently, a correspondence analysis, and the values of lexical factors were correlated with cortical and subcortical structural measures as well as with TSIA and psychological scores. Overall, the reciprocal relationships among the measures of neurobiological structures, the capacity of identifying, describing and regulating emotions, and the ability of translating the emotional experiences into verbal forms, allowed to identify a complex pattern of biopsychosocial aspects, getting light on referential processes characterizing the verbally and not-verbally express emotions. Psychology Hub, Vol. 38 No. 2 (2021)

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  • Authors: A Pascarella and F Pitolli;

    Understanding brain function from magneto-electroencephalographic (M/EEG) measurements requires advanced mathematical and signal processing tools. Although the analysis of M/EEG data at sensors level sheds light on important brain mechanisms, full exploitation of the information contained in such brain data could be achieved by reconstructing the active neural sources from M/EEG measurements. This involves solving an ill-posed and ill-conditioned inverse problem in which not only the identification of the most suitable inversion method [1, 2] but also the calibration of the regularization parameters is of paramount importance. Once time series representing brain activity are available, a next step is to develop tools to extract meaningful information that characterizes brain activity [3, 4], for example, when the subject under study is affected by diseases that impair brain function. The mini-symposium brings together researchers from various disciplines who have developed methodologies that are being successfully used for the analysis of the M/EEG data, the solution of the underlying inverse problem and in the definition of brain fingerprint. The purpose is not only to present the latest research results in this area but also to create a fruitful environment for the development of new ideas.

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    Conference object . 2023
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      Conference object . 2023
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    Authors: Brébion G; Núñez C; Lombardini F; Senior C; +4 Authors

    BACKGROUND: Depressive symptoms are known to affect memory efficiency in various populations. More specifically, several studies conducted in patients suffering from schizophrenia have indicated that memory efficiency is affected by depressed mood in female patients and by anxiety in male patients. We investigated, using neuroimaging techniques, whether similar gender-specific associations with subclinical depression and anxiety could be observed in a non-clinical sample. METHOD: Forty-five healthy Spanish-speaking individuals (23 females) were administered a verbal memory task. Lists of high- and low-frequency words were presented. Immediate free recall was requested after the learning of each list, and a yes/no recognition task was completed during the acquisition of the fMRI data. RESULTS: Regression analyses revealed that higher depression scores in women, and higher anxiety scores in men, were associated with poorer recall. In women, higher depression scores were further associated with decreased cerebral activity in the right temporoparietal junction, left inferior occipitotemporal gyrus, bilateral thalamus, and left anterior cingulate during correct recognition of target words. In men, anxiety scores were not associated with any cerebral activity. CONCLUSIONS: Subclinical depression in women appears to affect memory efficiency by impacting cerebral regions specifically recruited for the cognitive demands of the task, as well as cerebral regions more generally involved in arousal, decision-making, and emotional regulation. Anxiety in men might impact the encoding memory processes. The results, although preliminary, suggest that gender differences may need to be taken into account when developing strategies for the cognitive and pharmacological remediation of memory impairment.

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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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