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diffusion tensor imaging (dti)  (Chemie GmbH)

 
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    Chemie GmbH diffusion tensor imaging (dti)
    Diffusion Tensor Imaging (Dti), supplied by Chemie GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/diffusion+tensor+imaging+dti/diffusion+tensor+imaging++dti+/pm16528640-20-0-36
    Average 90 stars, based on 1 article reviews
    diffusion tensor imaging (dti) - by Bioz Stars, 2026-11
    90/100 stars

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    Related Articles

    Diffusion-based Assay:

    Article Title: [Diffusion tensor imaging (DTI) of the corpus callosum may further elucidate development of brain lateralization].
    Article Snippet: mehr und mehr, auch das Gehirn von Kindern im Verlauf der Ent− wicklung zu untersuchen und darzustellen.. Obwohl hinsichtlich funktioneller Fragestellungen weiterhin die fortschrittlichen Me− thoden des EEG (z.B.. 1brain mapping“ mit Algorithmen zur Loka− lisation hirnelektrischer Aktivität) der Vorzug zu geben ist, vor allem wenn es um hohe zeitliche Auflösung bei der Erfassung kognitiver Informationsverarbeitung geht, so sind magnetreso− nanz−tomographische Verfahren wie MRT, fMRT, MR−Spektro− skopie und Diffusion Tensor Imaging (DTI) mittlerweile gut ge− eignet, makrostrukturelle, gewisse mikrostrukturelle sowie zeit− lich grob aufgelöste funktionelle Aspekte des kindlichen Gehirns zu betrachten.

    Imaging:

    Article Title: [Diffusion tensor imaging (DTI) of the corpus callosum may further elucidate development of brain lateralization].
    Article Snippet: mehr und mehr, auch das Gehirn von Kindern im Verlauf der Ent− wicklung zu untersuchen und darzustellen.. Obwohl hinsichtlich funktioneller Fragestellungen weiterhin die fortschrittlichen Me− thoden des EEG (z.B.. 1brain mapping“ mit Algorithmen zur Loka− lisation hirnelektrischer Aktivität) der Vorzug zu geben ist, vor allem wenn es um hohe zeitliche Auflösung bei der Erfassung kognitiver Informationsverarbeitung geht, so sind magnetreso− nanz−tomographische Verfahren wie MRT, fMRT, MR−Spektro− skopie und Diffusion Tensor Imaging (DTI) mittlerweile gut ge− eignet, makrostrukturelle, gewisse mikrostrukturelle sowie zeit− lich grob aufgelöste funktionelle Aspekte des kindlichen Gehirns zu betrachten.



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    Image Search Results


    Correlation between diffusion tensor imaging (DTI) parameters and negative symptom (Scale for the Assessment of Negative Symptoms [SANS]) intercepts in the Johns Hopkins Schizophrenia Center dataset. DTI parameters were averaged across all sessions for each participant. (A) Longitudinal progression of symptoms. Empty circles at baseline show participants with no follow-ups. Trendlines show a linear fixed-effect model of parameter against session with random slopes and intercepts fit for every participant. Shaded bands show a 95% CI computed with parametric bootstrapping resampling residuals and random effects 1000 times. Neither parameter significantly varied with session (Scale for the Assessment of Positive Symptoms [SAPS] t 22.9 = −0.43, p = .66) (SANS t 27.3 = −0.92, p = .36). (B, C) Intercept was computed using a first-order linear model for each participant, with the baseline scan as time 0. Relationships with DTI parameters were tested with a linear model with age and sex and covariates. (B) Significant regions of interest are colored according to their t value. Multiple comparisons were corrected with the false discovery rate. (C) Scatter plots showing DTI parameters averaged across the white matter. Shaded bands show 95% CI computed with nonparametric bootstrap paired resampling with 1000 permutations. Mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) significantly increased with session. Fractional anisotropy (FA) did not significantly change. t Values and p values are shown in <xref ref-type=Table S10 . " width="100%" height="100%">

    Journal: Biological Psychiatry Global Open Science

    Article Title: Stable White Matter Structure in the First Three Years After Psychosis Onset

    doi: 10.1016/j.bpsgos.2025.100472

    Figure Lengend Snippet: Correlation between diffusion tensor imaging (DTI) parameters and negative symptom (Scale for the Assessment of Negative Symptoms [SANS]) intercepts in the Johns Hopkins Schizophrenia Center dataset. DTI parameters were averaged across all sessions for each participant. (A) Longitudinal progression of symptoms. Empty circles at baseline show participants with no follow-ups. Trendlines show a linear fixed-effect model of parameter against session with random slopes and intercepts fit for every participant. Shaded bands show a 95% CI computed with parametric bootstrapping resampling residuals and random effects 1000 times. Neither parameter significantly varied with session (Scale for the Assessment of Positive Symptoms [SAPS] t 22.9 = −0.43, p = .66) (SANS t 27.3 = −0.92, p = .36). (B, C) Intercept was computed using a first-order linear model for each participant, with the baseline scan as time 0. Relationships with DTI parameters were tested with a linear model with age and sex and covariates. (B) Significant regions of interest are colored according to their t value. Multiple comparisons were corrected with the false discovery rate. (C) Scatter plots showing DTI parameters averaged across the white matter. Shaded bands show 95% CI computed with nonparametric bootstrap paired resampling with 1000 permutations. Mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) significantly increased with session. Fractional anisotropy (FA) did not significantly change. t Values and p values are shown in Table S10 .

    Article Snippet: Figure 2 Correlation between diffusion tensor imaging (DTI) parameters and negative symptom (Scale for the Assessment of Negative Symptoms [SANS]) intercepts in the Johns Hopkins Schizophrenia Center dataset.

    Techniques: Diffusion-based Assay, Imaging

    Correlations between diffusion tensor imaging (DTI) parameters and the 8-item Positive and Negative Syndrome Scale-Negative (PANSS8-N) follow-up score in the Tracking Outcomes in Psychosis dataset. DTI measures were averaged across sessions per participant. (A) Longitudinal progression of symptoms. Empty circles at baseline show participants with no follow-ups. Trendlines show a linear fixed-effect model of parameter against session with random intercepts fit for every participant. Shaded bands show a 95% CI computed with parametric bootstrapping resampling residuals and random effects 1000 times. PANSS8 Positive (PANSS8-P) was significantly lower at the second session ( t 21.0 = −10.9, p < .001). PANSS8-N did not significantly change ( t 21.0 = −0.70, p = .49). (B–D) Participants grouped based on whether their PANSS8-N score at follow-up was equal to 3, the lowest possible score (remission). Relationships with DTI parameters tested with a linear model with age and sex and covariates. Regions of interest from each panel come from different nested hierarchical layers at successively higher resolutions. Multiple comparisons for each layer were corrected with the false discovery rate. All comparisons shown are significant. t Values and p values are shown in <xref ref-type=Table S9 . FA, fractional anisotropy. " width="100%" height="100%">

    Journal: Biological Psychiatry Global Open Science

    Article Title: Stable White Matter Structure in the First Three Years After Psychosis Onset

    doi: 10.1016/j.bpsgos.2025.100472

    Figure Lengend Snippet: Correlations between diffusion tensor imaging (DTI) parameters and the 8-item Positive and Negative Syndrome Scale-Negative (PANSS8-N) follow-up score in the Tracking Outcomes in Psychosis dataset. DTI measures were averaged across sessions per participant. (A) Longitudinal progression of symptoms. Empty circles at baseline show participants with no follow-ups. Trendlines show a linear fixed-effect model of parameter against session with random intercepts fit for every participant. Shaded bands show a 95% CI computed with parametric bootstrapping resampling residuals and random effects 1000 times. PANSS8 Positive (PANSS8-P) was significantly lower at the second session ( t 21.0 = −10.9, p < .001). PANSS8-N did not significantly change ( t 21.0 = −0.70, p = .49). (B–D) Participants grouped based on whether their PANSS8-N score at follow-up was equal to 3, the lowest possible score (remission). Relationships with DTI parameters tested with a linear model with age and sex and covariates. Regions of interest from each panel come from different nested hierarchical layers at successively higher resolutions. Multiple comparisons for each layer were corrected with the false discovery rate. All comparisons shown are significant. t Values and p values are shown in Table S9 . FA, fractional anisotropy.

    Article Snippet: Figure 2 Correlation between diffusion tensor imaging (DTI) parameters and negative symptom (Scale for the Assessment of Negative Symptoms [SANS]) intercepts in the Johns Hopkins Schizophrenia Center dataset.

    Techniques: Diffusion-based Assay, Imaging