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diffusion tensor imaging (dti)  (WhiteMatter Labs GmbH)

 
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    WhiteMatter Labs GmbH diffusion tensor imaging (dti)
    Diffusion Tensor Imaging (Dti), supplied by WhiteMatter Labs 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/pm40075890-40-1-11
    Average 90 stars, based on 1 article reviews
    diffusion tensor imaging (dti) - by Bioz Stars, 2026-09
    90/100 stars

    Images

    Related Articles

    Diffusion-based Assay:

    Article Title: White Matter Microstructure and Information Processing at the Completion of Chemotherapy-Only Treatment for Pediatric Acute Lymphoblastic Leukemia.
    Article Snippet: Little is known about white matter microstructure and its role in information processing abilities of children treated for acute lymphoblastic leukemia (ALL) early posttreatment.. Twenty-one survivors of ALL and 18 controls (7–16 years) underwent neurocognitive assessment.. A subsample underwent diffusion-weighted magnetic resonance imaging.

    Imaging:

    Article Title: White Matter Microstructure and Information Processing at the Completion of Chemotherapy-Only Treatment for Pediatric Acute Lymphoblastic Leukemia.
    Article Snippet: Little is known about white matter microstructure and its role in information processing abilities of children treated for acute lymphoblastic leukemia (ALL) early posttreatment.. Twenty-one survivors of ALL and 18 controls (7–16 years) underwent neurocognitive assessment.. A subsample underwent diffusion-weighted magnetic resonance imaging.

    other:

    Article Title: Pain-Related White-Matter Changes Following Mild Traumatic Brain Injury: A Longitudinal Diffusion Tensor Imaging Pilot Study.
    Article Snippet: The classic diffusion tensor imaging (DTI) has been widely used in whitematter diseases and was found to have adequate diagnostic sensitivity to microstructural changes in the brain after mTBI [35–44].

    Article Title: A mesoscale connectome of the mouse brain.
    Article Snippet: At the macroscale, longrange, region-to-region connections can be inferred from imaging whitematter fibre tracts through diffusion tensor imaging (DTI) in the living brain10.



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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

    Comparison of protocols used by 3 research groups for ultrahigh-resolution imaging of formalin-fixed ex vivo human brain specimens.

    Journal: Frontiers in Human Neuroscience

    Article Title: Ultrahigh-resolution 7-Tesla anatomic magnetic resonance imaging and diffusion tensor imaging of ex vivo formalin-fixed human brainstem-cerebellum complex

    doi: 10.3389/fnhum.2024.1484431

    Figure Lengend Snippet: Comparison of protocols used by 3 research groups for ultrahigh-resolution imaging of formalin-fixed ex vivo human brain specimens.

    Article Snippet: Diffusion tensor imaging (DTI) lasted 48 h and 48 min, and subsequent anatomical imaging was performed using a 7-Tesla (7T) MRI system (Bruker Biospec 70/30 with 30-cm bore size) equipped with a 70-mm volume coil.

    Techniques: Comparison, Imaging, Ex Vivo, Saline, Sequencing, Diffusion-based Assay, Software