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  1. Article ; Online: Reassessing rumination: Validity of the Day Reconstruction Method for Rumination (DRM-R) to assess episodes of rumination in daily life.

    Rosenfeld, Eve A / Lyman, Cassondra / Wojcik, Curtis M / Macia, Kathryn S / Roberts, John E

    Psychological assessment

    2023  Volume 35, Issue 12, Page(s) 1098–1107

    Abstract: Rumination is a robust vulnerability to depression and potential treatment target. However, we know relatively little about rumination in daily life. This study tested the validity of a new approach for assessing daily episodes of rumination, the Day ... ...

    Abstract Rumination is a robust vulnerability to depression and potential treatment target. However, we know relatively little about rumination in daily life. This study tested the validity of a new approach for assessing daily episodes of rumination, the Day Reconstruction Method for Rumination (DRM-R). Participants (
    MeSH term(s) Humans ; Mental Disorders ; Anxiety ; Cognition ; Neuroticism
    Language English
    Publishing date 2023-09-28
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1000939-5
    ISSN 1939-134X ; 1040-3590
    ISSN (online) 1939-134X
    ISSN 1040-3590
    DOI 10.1037/pas0001282
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2645: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  2. Article ; Online: MRI-based thalamic volumetry in multiple sclerosis using FSL-FIRST: Systematic assessment of common error modes.

    Lyman, Cassondra / Lee, Dongchan / Ferrari, Hannah / Fuchs, Tom A / Bergsland, Niels / Jakimovski, Dejan / Weinstock-Guttmann, Bianca / Zivadinov, Robert / Dwyer, Michael G

    Journal of neuroimaging : official journal of the American Society of Neuroimaging

    2021  Volume 32, Issue 2, Page(s) 245–252

    Abstract: Background and purpose: FSL's FMRIB's Integrated Registration and Segmentation Tool (FSL-FIRST) is a widely used and well-validated tool. Automated thalamic segmentation is a common application and an important longitudinal measure for multiple ... ...

    Abstract Background and purpose: FSL's FMRIB's Integrated Registration and Segmentation Tool (FSL-FIRST) is a widely used and well-validated tool. Automated thalamic segmentation is a common application and an important longitudinal measure for multiple sclerosis (MS). However, FSL-FIRST's algorithm is based on shape models derived from non-MS groups. As such, the present study sought to systematically assess common thalamic segmentation errors made by FSL-FIRST on MRIs from people with multiple sclerosis (PwMS).
    Methods: FSL-FIRST was applied to generate thalamic segmentation masks for 890 MR images in PwMS. Images and masks were reviewed systematically to classify and quantify errors, as well as associated anatomical variations and MRI abnormalities. For cases with overt errors (n = 362), thalamic masks were corrected and quantitative volumetric differences were calculated.
    Results: In the entire quantitative volumetric group, the mean volumetric error of FSL-FIRST was 2.74% (0.360 ml): among only corrected cases, the mean volumetric error was 6.79% (0.894 ml). The average percent volumetric error associated with seven error types, two anatomical variants, and motions artifacts are reported. Additional analyses showed that the presence of motion artifacts or anatomical variations significantly increased the probability of error (χ
    Conclusions: In PwMS, FSL-FIRST thalamic segmentation miscalculates thalamic volumetry in a predictable fashion, and may be biased to overestimate highly atrophic thalami. As such, it is recommended that segmentations be reviewed and corrected manually when appropriate for specific studies.
    MeSH term(s) Algorithms ; Atrophy/diagnostic imaging ; Atrophy/pathology ; Humans ; Magnetic Resonance Imaging/methods ; Multiple Sclerosis/diagnostic imaging ; Multiple Sclerosis/pathology ; Thalamus/diagnostic imaging ; Thalamus/pathology
    Language English
    Publishing date 2021-11-12
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 1071724-9
    ISSN 1552-6569 ; 1051-2284
    ISSN (online) 1552-6569
    ISSN 1051-2284
    DOI 10.1111/jon.12947
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 3211: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  3. Article ; Online: DeepGRAI (Deep Gray Rating via Artificial Intelligence): Fast, feasible, and clinically relevant thalamic atrophy measurement on clinical quality T2-FLAIR MRI in multiple sclerosis.

    Dwyer, Michael / Lyman, Cassondra / Ferrari, Hannah / Bergsland, Niels / Fuchs, Tom A / Jakimovski, Dejan / Schweser, Ferdinand / Weinstock-Guttmann, Bianca / Benedict, Ralph H B / Riolo, Jon / Silva, Diego / Zivadinov, Robert

    NeuroImage. Clinical

    2021  Volume 30, Page(s) 102652

    Abstract: Background: Thalamic volume loss is a key marker of neurodegeneration in multiple sclerosis (MS). T2-FLAIR MRI is a common denominator in clinical routine MS imaging, but current methods for thalamic volumetry are not applicable to it.: Objective: To ...

    Abstract Background: Thalamic volume loss is a key marker of neurodegeneration in multiple sclerosis (MS). T2-FLAIR MRI is a common denominator in clinical routine MS imaging, but current methods for thalamic volumetry are not applicable to it.
    Objective: To develop and validate a robust algorithm to measure thalamic volume using clinical routine T2-FLAIR MRI.
    Methods: A dual-stage deep learning approach based on 3D U-net (DeepGRAI - Deep Gray Rating via Artificial Intelligence) was created and trained/validated/tested on 4,590 MRI exams (4288 2D-FLAIR, 302 3D-FLAIR) from 59 centers (80/10/10 train/validation/test split). As training/test targets, FIRST was used to generate thalamic masks from 3D T1 images. Masks were reviewed, corrected, and aligned into T2-FLAIR space. Additional validation was performed to assess inter-scanner reliability (177 subjects at 1.5 T and 3 T within one week) and scan-rescan-reliability (5 subjects scanned, repositioned, and then re-scanned). A longitudinal dataset including assessment of disability and cognition was used to evaluate the predictive value of the approach.
    Results: DeepGRAI automatically quantified thalamic volume in approximately 7 s per case, and has been made publicly available. Accuracy on T2-FLAIR relative to 3D T1 FIRST was 99.4% (r = 0.94, p < 0.001,TPR = 93.0%, FPR = 0.3%). Inter-scanner error was 3.21%. Scan-rescan error with repositioning was 0.43%. DeepGRAI-derived thalamic volume was associated with disability (r = -0.427,p < 0.001) and cognition (r = -0.537,p < 0.001), and was a significant predictor of longitudinal cognitive decline (R
    Conclusions: DeepGRAI provides fast, reliable, and clinically relevant thalamic volume measurement on multicenter clinical-quality T2-FLAIR images. This indicates potential for real-world thalamic volumetry, as well as quantification on legacy datasets without 3D T1 imaging.
    MeSH term(s) Artificial Intelligence ; Atrophy/pathology ; Brain/diagnostic imaging ; Brain/pathology ; Humans ; Magnetic Resonance Imaging ; Multiple Sclerosis/diagnostic imaging ; Multiple Sclerosis/pathology ; Reproducibility of Results
    Language English
    Publishing date 2021-03-29
    Publishing country Netherlands
    Document type Journal Article ; Multicenter Study ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2701571-3
    ISSN 2213-1582 ; 2213-1582
    ISSN (online) 2213-1582
    ISSN 2213-1582
    DOI 10.1016/j.nicl.2021.102652
    Database MEDical Literature Analysis and Retrieval System OnLINE

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