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diffusion tensor imaging (dti) parameters  (Johns Hopkins HealthCare)

 
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    Johns Hopkins HealthCare diffusion tensor imaging (dti) parameters
    Correlation between <t>diffusion</t> <t>tensor</t> <t>imaging</t> <t>(DTI)</t> 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="250" height="auto" />
    Diffusion Tensor Imaging (Dti) Parameters, supplied by Johns Hopkins HealthCare, 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)+parameters/diffusion+tensor+imaging/pmc11994302-158-4-23
    Average 90 stars, based on 1 article reviews
    diffusion tensor imaging (dti) parameters - by Bioz Stars, 2026-11
    90/100 stars

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    1) Product Images from "Stable White Matter Structure in the First Three Years After Psychosis Onset"

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

    Journal: Biological Psychiatry Global Open Science

    doi: 10.1016/j.bpsgos.2025.100472

    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 . " title="Correlation between diffusion tensor imaging (DTI) parameters and negative symptom (Scale for the Assessment ..." property="contentUrl" width="100%" height="100%"/>
    Figure Legend 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 .

    Techniques Used: 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. " title="Correlations between diffusion tensor imaging (DTI) parameters and the 8-item Positive and Negative Syndrome ..." property="contentUrl" width="100%" height="100%"/>
    Figure Legend 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.

    Techniques Used: Diffusion-based Assay, Imaging

    Related Articles

    other:

    Article Title: Neuroimaging of prenatal drug exposure
    Article Snippet: Assisted by our collaborators at the University of Florida (S.B., K.P.) and Johns Hopkins University (S.M.), we have also incorporated diffusion tensor imaging (DTI) data acquisition into the scanning protocol.

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

    Article Title: Navigating the unseen peril: safeguarding medical imaging in the age of AI
    Article Snippet: Johns hopkins diffusion tensor imaging (DTI) / Laboratory of brain anatomical MRI High resolution MRI scans to facilitate research in DTI data processing and analysis or as control data.

    Article Title: Reading-related Brain Function Restored to Normal After Articulation Training in Patients with Cleft Lip and Palate: An fMRI Study.
    Article Snippet: The templates were acquired from Johns Hopkins University (JHU) diffusion tensor imaging (DTI)-based white-matter atlases [21] and the Automated Anatomical Labeling (AAL) atlas (https:// www. gin. cnrs. fr/ en/ tools/ aal/) [22].

    Diffusion-based Assay:

    Article Title: Advances in magnetic resonance imaging of the developing brain and its applications in pediatrics.
    Article Snippet: .. Additionally, the team at Johns Hopkins University (JHU) utilized diffusion MRI to parcellate the brain and proposed a neonatal DTI template [131, 167]. ..

    Article Title: Diffusion tensor imaging reveals Papez circuit lesions in severe traumatic brain injury with memory disorder.
    Article Snippet: Annals of Physical and Rehabilitation Medicine 68 (2025) 101927 Available online at ScienceDirect www.sciencedirect.com Letter to the editor Diffusion tensor imaging reveals Papez circuit lesions in severe traumatic brain injury with memory disorder A R T I C L E I N F O impairment (EMI), using a z-score cut-off of 1.5 from anterograde Abbreviations: AD, axial diffusivity; DTI, diffusion te anisotropy; FDR, false discovery rate; EMI, Episodic Me sive care unit; JHU, Johns Hopkins University; MD, mean onance imaging; RD, radial diffusivity; SEL, socio-eco brain injury Trial registration: ClinicalTrials.gov, NCT00577954.. Reg https://doi.org/10.1016/j.rehab.2025.101927 1877-0657/© 2025 Elsevier Masson SAS.. All rights are re

    Article Title: Different patterns of association between white matter microstructure and plasma unsaturated fatty acids in those with high risk for psychosis and healthy participants
    Article Snippet: .. The Johns Hopkins University diffusion tensor imaging (DTI)-based WM atlas in FSL toolbox (ICBM-DTI-81 atlas) was used to identify the localisation of abnormal WM fibre tracts. ..

    Article Title: Navigating the unseen peril: safeguarding medical imaging in the age of AI
    Article Snippet: USC stevens neuroimaging and informatics institute image and data archive (IDA) , Neuroscience data on development, aging, and disease progression. , Data collected from 97,128 subjects for 151 studies in 165 countries. , The Laboratory of Neuro Imaging (LONI) , Provides tools for de-identifying, integrating, searching, visualizing and sharing neuroscience data , Investigators maintain data control. Robust, reliable infrastructure protects and preserves research data. , 40-page user manual explains the upload and download process.. .. Johns hopkins diffusion tensor imaging (DTI) / Laboratory of brain anatomical MRI , High resolution MRI scans to facilitate research in DTI data processing and analysis or as control data. , Raw and processed normal population DTI data. Basic imaging parameters provided, with details available. , - , Outdated website with dead links. Embedded unsupported Adobe Flash elements make it vulnerable to attack , - , Open to the public once a user is registered. ..

    Magnetic Resonance Imaging:

    Article Title: Advances in magnetic resonance imaging of the developing brain and its applications in pediatrics.
    Article Snippet: .. Additionally, the team at Johns Hopkins University (JHU) utilized diffusion MRI to parcellate the brain and proposed a neonatal DTI template [131, 167]. ..

    Article Title: Navigating the unseen peril: safeguarding medical imaging in the age of AI
    Article Snippet: USC stevens neuroimaging and informatics institute image and data archive (IDA) , Neuroscience data on development, aging, and disease progression. , Data collected from 97,128 subjects for 151 studies in 165 countries. , The Laboratory of Neuro Imaging (LONI) , Provides tools for de-identifying, integrating, searching, visualizing and sharing neuroscience data , Investigators maintain data control. Robust, reliable infrastructure protects and preserves research data. , 40-page user manual explains the upload and download process.. .. Johns hopkins diffusion tensor imaging (DTI) / Laboratory of brain anatomical MRI , High resolution MRI scans to facilitate research in DTI data processing and analysis or as control data. , Raw and processed normal population DTI data. Basic imaging parameters provided, with details available. , - , Outdated website with dead links. Embedded unsupported Adobe Flash elements make it vulnerable to attack , - , Open to the public once a user is registered. ..

    Imaging:

    Article Title: Diffusion tensor imaging reveals Papez circuit lesions in severe traumatic brain injury with memory disorder.
    Article Snippet: Annals of Physical and Rehabilitation Medicine 68 (2025) 101927 Available online at ScienceDirect www.sciencedirect.com Letter to the editor Diffusion tensor imaging reveals Papez circuit lesions in severe traumatic brain injury with memory disorder A R T I C L E I N F O impairment (EMI), using a z-score cut-off of 1.5 from anterograde Abbreviations: AD, axial diffusivity; DTI, diffusion te anisotropy; FDR, false discovery rate; EMI, Episodic Me sive care unit; JHU, Johns Hopkins University; MD, mean onance imaging; RD, radial diffusivity; SEL, socio-eco brain injury Trial registration: ClinicalTrials.gov, NCT00577954.. Reg https://doi.org/10.1016/j.rehab.2025.101927 1877-0657/© 2025 Elsevier Masson SAS.. All rights are re

    Article Title: Different patterns of association between white matter microstructure and plasma unsaturated fatty acids in those with high risk for psychosis and healthy participants
    Article Snippet: .. The Johns Hopkins University diffusion tensor imaging (DTI)-based WM atlas in FSL toolbox (ICBM-DTI-81 atlas) was used to identify the localisation of abnormal WM fibre tracts. ..

    Article Title: Navigating the unseen peril: safeguarding medical imaging in the age of AI
    Article Snippet: USC stevens neuroimaging and informatics institute image and data archive (IDA) , Neuroscience data on development, aging, and disease progression. , Data collected from 97,128 subjects for 151 studies in 165 countries. , The Laboratory of Neuro Imaging (LONI) , Provides tools for de-identifying, integrating, searching, visualizing and sharing neuroscience data , Investigators maintain data control. Robust, reliable infrastructure protects and preserves research data. , 40-page user manual explains the upload and download process.. .. Johns hopkins diffusion tensor imaging (DTI) / Laboratory of brain anatomical MRI , High resolution MRI scans to facilitate research in DTI data processing and analysis or as control data. , Raw and processed normal population DTI data. Basic imaging parameters provided, with details available. , - , Outdated website with dead links. Embedded unsupported Adobe Flash elements make it vulnerable to attack , - , Open to the public once a user is registered. ..

    Control:

    Article Title: Navigating the unseen peril: safeguarding medical imaging in the age of AI
    Article Snippet: USC stevens neuroimaging and informatics institute image and data archive (IDA) , Neuroscience data on development, aging, and disease progression. , Data collected from 97,128 subjects for 151 studies in 165 countries. , The Laboratory of Neuro Imaging (LONI) , Provides tools for de-identifying, integrating, searching, visualizing and sharing neuroscience data , Investigators maintain data control. Robust, reliable infrastructure protects and preserves research data. , 40-page user manual explains the upload and download process.. .. Johns hopkins diffusion tensor imaging (DTI) / Laboratory of brain anatomical MRI , High resolution MRI scans to facilitate research in DTI data processing and analysis or as control data. , Raw and processed normal population DTI data. Basic imaging parameters provided, with details available. , - , Outdated website with dead links. Embedded unsupported Adobe Flash elements make it vulnerable to attack , - , Open to the public once a user is registered. ..



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    diffusion tensor imaging (dti) parameters  (Johns Hopkins HealthCare)
    90
    Johns Hopkins HealthCare diffusion tensor imaging (dti) parameters
    Correlation between <t>diffusion</t> <t>tensor</t> <t>imaging</t> <t>(DTI)</t> 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="250" height="auto" />
    Diffusion Tensor Imaging (Dti) Parameters, supplied by Johns Hopkins HealthCare, 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)+parameters/diffusion+tensor+imaging/pmc11994302-158-4-23
    Average 90 stars, based on 1 article reviews
    diffusion tensor imaging (dti) parameters - by Bioz Stars, 2026-11
    90/100 stars
    		  Buy from Supplier

    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