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in house developed neurophysiological biomarker toolbox nbt  (MathWorks Inc)


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    MathWorks Inc in house developed neurophysiological biomarker toolbox nbt
    Both seizure zone ( A , B , E–H ) and non-seizure zone ( C , D , I-L ) electrodes show increases in E/I HLP and E+I HLS during ictal periods. <t>Biomarker</t> values were averaged across seizure (SOZ)/non-seizure zone (nSOZ) electrodes, and a paired t -test was run across subject-level averages. Circles ( A–D ) correspond to frequency bins where comparisons stayed significant after FDR correction ( q =0.05). ( E–L ) A representative subject was selected, and sample traces of the oscillatory power time series were plotted for a selected frequency band (10.5-13.4 Hz), along with the bimodal power distributions, to illustrate the changes in oscillatory dynamics between the ictal and interictal states.
    In House Developed Neurophysiological Biomarker Toolbox Nbt, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 452 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/in+house+developed+neurophysiological+biomarker+toolbox+nbt/Instrument+Control+Toolbox/bio_rxiv__2025__08__15__670484-178-7-15
    Average 96 stars, based on 452 article reviews
    in house developed neurophysiological biomarker toolbox nbt - by Bioz Stars, 2026-09
    96/100 stars

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    1) Product Images from "E/I ratio and net E+I strength are differentially affected across brain disorders"

    Article Title: E/I ratio and net E+I strength are differentially affected across brain disorders

    Journal: bioRxiv

    doi: 10.1101/2025.08.15.670484

    Both seizure zone ( A , B , E–H ) and non-seizure zone ( C , D , I-L ) electrodes show increases in E/I HLP and E+I HLS during ictal periods. Biomarker values were averaged across seizure (SOZ)/non-seizure zone (nSOZ) electrodes, and a paired t -test was run across subject-level averages. Circles ( A–D ) correspond to frequency bins where comparisons stayed significant after FDR correction ( q =0.05). ( E–L ) A representative subject was selected, and sample traces of the oscillatory power time series were plotted for a selected frequency band (10.5-13.4 Hz), along with the bimodal power distributions, to illustrate the changes in oscillatory dynamics between the ictal and interictal states.
    Figure Legend Snippet: Both seizure zone ( A , B , E–H ) and non-seizure zone ( C , D , I-L ) electrodes show increases in E/I HLP and E+I HLS during ictal periods. Biomarker values were averaged across seizure (SOZ)/non-seizure zone (nSOZ) electrodes, and a paired t -test was run across subject-level averages. Circles ( A–D ) correspond to frequency bins where comparisons stayed significant after FDR correction ( q =0.05). ( E–L ) A representative subject was selected, and sample traces of the oscillatory power time series were plotted for a selected frequency band (10.5-13.4 Hz), along with the bimodal power distributions, to illustrate the changes in oscillatory dynamics between the ictal and interictal states.

    Techniques Used: Biomarker Discovery

    (A) During ictal periods, the seizure zone (SOZ) exhibits significantly higher E/I HLP compared to non-seizure zone (nSOZ). (C) During interictal periods, on the other hand, the seizure zone has lower E/I HLP . (B , D) E+I HLS is significantly higher in the seizure zone regardless of the seizure status. Biomarker values were averaged across seizure (SOZ)/non-seizure zone (nSOZ) electrodes, and a paired t -test was run across subject-level averages. Circles ( A–D ) correspond to frequency bins where comparisons stayed significant after FDR correction ( q = 0.05). ( E–L ) A representative subject was selected, and sample traces of the oscillatory power time series were plotted for a selected frequency band (10.5-13.4 Hz), along with the bimodal power distributions, to illustrate differences in oscillatory dynamics between SOZ and nSOZ.
    Figure Legend Snippet: (A) During ictal periods, the seizure zone (SOZ) exhibits significantly higher E/I HLP compared to non-seizure zone (nSOZ). (C) During interictal periods, on the other hand, the seizure zone has lower E/I HLP . (B , D) E+I HLS is significantly higher in the seizure zone regardless of the seizure status. Biomarker values were averaged across seizure (SOZ)/non-seizure zone (nSOZ) electrodes, and a paired t -test was run across subject-level averages. Circles ( A–D ) correspond to frequency bins where comparisons stayed significant after FDR correction ( q = 0.05). ( E–L ) A representative subject was selected, and sample traces of the oscillatory power time series were plotted for a selected frequency band (10.5-13.4 Hz), along with the bimodal power distributions, to illustrate differences in oscillatory dynamics between SOZ and nSOZ.

    Techniques Used: Biomarker Discovery

    (A) Logistic regression models reveal that patients with Alzheimer’s, ADHD, or ASD can be distinguished from healthy controls by alterations in E/I HLP and E+I HLS . Significance for each disorder was assessed by comparing the average AUC-ROC (black dot) across 50 cross-validation folds against a null distribution generated with 500 random permutations (gray violin plot). Black asterisk reflects significance after FDR ( q = 0.05) correction. (B) When training logistic regression separately for E/I HLP and E+I HLS , we found Alzheimer’s disease showing mostly disruptions in E/I ratio, while ASD and ADHD more prominent changes in E+I density. To illustrate how the relative contribution of E/I HLP and E+I HLS differs across the three disorders, we plotted disorder-specific case-control averages of each biomarker across the frequency spectrum ( C–H ). Asterisks above a trace mark frequency bins in which patient and control means differ significantly (two-sample t -test, FDR-corrected at q = 0.05, performed separately for every disorder-by-biomarker combination).
    Figure Legend Snippet: (A) Logistic regression models reveal that patients with Alzheimer’s, ADHD, or ASD can be distinguished from healthy controls by alterations in E/I HLP and E+I HLS . Significance for each disorder was assessed by comparing the average AUC-ROC (black dot) across 50 cross-validation folds against a null distribution generated with 500 random permutations (gray violin plot). Black asterisk reflects significance after FDR ( q = 0.05) correction. (B) When training logistic regression separately for E/I HLP and E+I HLS , we found Alzheimer’s disease showing mostly disruptions in E/I ratio, while ASD and ADHD more prominent changes in E+I density. To illustrate how the relative contribution of E/I HLP and E+I HLS differs across the three disorders, we plotted disorder-specific case-control averages of each biomarker across the frequency spectrum ( C–H ). Asterisks above a trace mark frequency bins in which patient and control means differ significantly (two-sample t -test, FDR-corrected at q = 0.05, performed separately for every disorder-by-biomarker combination).

    Techniques Used: Biomarker Discovery, Generated, Control

    Related Articles

    Biomarker Discovery:

    Article Title: Qualitative EEG abnormalities signal a shift towards inhibition-dominated brain networks. Results from the EU-AIMS LEAP studies
    Article Snippet: EEGs were recorded during 3–5 minutes of eyes-closed rest at the UMC Utrecht using A 64-channel BioSemi EEG system at a sampling rate of 2048 Hz and common mode sense (CMS) reference electrode. .. EEG analyses were initially pre-processed using the in-house developed Neurophysiological Biomarker Toolbox (NBT) written in MATLAB. ..

    Article Title: E/I ratio and net E+I strength are differentially affected across brain disorders
    Article Snippet: In brief, EEGs were recorded during 3–5 minutes of eyes-closed rest at the UMC Utrecht using A 64-channel BioSemi EEG system at a sampling rate of 2048 Hz and common mode sense (CMS) reference electrode. .. EEG analyses were initially pre-processed using the in-house developed Neurophysiological Biomarker Toolbox (NBT) written in MATLAB. ..



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    in house developed neurophysiological biomarker toolbox nbt  (MathWorks Inc)
    96
    MathWorks Inc in house developed neurophysiological biomarker toolbox nbt
    Both seizure zone ( A , B , E–H ) and non-seizure zone ( C , D , I-L ) electrodes show increases in E/I HLP and E+I HLS during ictal periods. <t>Biomarker</t> values were averaged across seizure (SOZ)/non-seizure zone (nSOZ) electrodes, and a paired t -test was run across subject-level averages. Circles ( A–D ) correspond to frequency bins where comparisons stayed significant after FDR correction ( q =0.05). ( E–L ) A representative subject was selected, and sample traces of the oscillatory power time series were plotted for a selected frequency band (10.5-13.4 Hz), along with the bimodal power distributions, to illustrate the changes in oscillatory dynamics between the ictal and interictal states.
    In House Developed Neurophysiological Biomarker Toolbox Nbt, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/in+house+developed+neurophysiological+biomarker+toolbox+nbt/Instrument+Control+Toolbox/bio_rxiv__2025__08__15__670484-178-7-15
    Average 96 stars, based on 1 article reviews
    in house developed neurophysiological biomarker toolbox nbt - by Bioz Stars, 2026-09
    96/100 stars
    		  Buy from Supplier

    Image Search Results


    Both seizure zone ( A , B , E–H ) and non-seizure zone ( C , D , I-L ) electrodes show increases in E/I HLP and E+I HLS during ictal periods. Biomarker values were averaged across seizure (SOZ)/non-seizure zone (nSOZ) electrodes, and a paired t -test was run across subject-level averages. Circles ( A–D ) correspond to frequency bins where comparisons stayed significant after FDR correction ( q =0.05). ( E–L ) A representative subject was selected, and sample traces of the oscillatory power time series were plotted for a selected frequency band (10.5-13.4 Hz), along with the bimodal power distributions, to illustrate the changes in oscillatory dynamics between the ictal and interictal states.

    Journal: bioRxiv

    Article Title: E/I ratio and net E+I strength are differentially affected across brain disorders

    doi: 10.1101/2025.08.15.670484

    Figure Lengend Snippet: Both seizure zone ( A , B , E–H ) and non-seizure zone ( C , D , I-L ) electrodes show increases in E/I HLP and E+I HLS during ictal periods. Biomarker values were averaged across seizure (SOZ)/non-seizure zone (nSOZ) electrodes, and a paired t -test was run across subject-level averages. Circles ( A–D ) correspond to frequency bins where comparisons stayed significant after FDR correction ( q =0.05). ( E–L ) A representative subject was selected, and sample traces of the oscillatory power time series were plotted for a selected frequency band (10.5-13.4 Hz), along with the bimodal power distributions, to illustrate the changes in oscillatory dynamics between the ictal and interictal states.

    Article Snippet: EEG analyses were initially pre-processed using the in-house developed Neurophysiological Biomarker Toolbox (NBT) written in MATLAB.

    Techniques: Biomarker Discovery

    (A) During ictal periods, the seizure zone (SOZ) exhibits significantly higher E/I HLP compared to non-seizure zone (nSOZ). (C) During interictal periods, on the other hand, the seizure zone has lower E/I HLP . (B , D) E+I HLS is significantly higher in the seizure zone regardless of the seizure status. Biomarker values were averaged across seizure (SOZ)/non-seizure zone (nSOZ) electrodes, and a paired t -test was run across subject-level averages. Circles ( A–D ) correspond to frequency bins where comparisons stayed significant after FDR correction ( q = 0.05). ( E–L ) A representative subject was selected, and sample traces of the oscillatory power time series were plotted for a selected frequency band (10.5-13.4 Hz), along with the bimodal power distributions, to illustrate differences in oscillatory dynamics between SOZ and nSOZ.

    Journal: bioRxiv

    Article Title: E/I ratio and net E+I strength are differentially affected across brain disorders

    doi: 10.1101/2025.08.15.670484

    Figure Lengend Snippet: (A) During ictal periods, the seizure zone (SOZ) exhibits significantly higher E/I HLP compared to non-seizure zone (nSOZ). (C) During interictal periods, on the other hand, the seizure zone has lower E/I HLP . (B , D) E+I HLS is significantly higher in the seizure zone regardless of the seizure status. Biomarker values were averaged across seizure (SOZ)/non-seizure zone (nSOZ) electrodes, and a paired t -test was run across subject-level averages. Circles ( A–D ) correspond to frequency bins where comparisons stayed significant after FDR correction ( q = 0.05). ( E–L ) A representative subject was selected, and sample traces of the oscillatory power time series were plotted for a selected frequency band (10.5-13.4 Hz), along with the bimodal power distributions, to illustrate differences in oscillatory dynamics between SOZ and nSOZ.

    Article Snippet: EEG analyses were initially pre-processed using the in-house developed Neurophysiological Biomarker Toolbox (NBT) written in MATLAB.

    Techniques: Biomarker Discovery

    (A) Logistic regression models reveal that patients with Alzheimer’s, ADHD, or ASD can be distinguished from healthy controls by alterations in E/I HLP and E+I HLS . Significance for each disorder was assessed by comparing the average AUC-ROC (black dot) across 50 cross-validation folds against a null distribution generated with 500 random permutations (gray violin plot). Black asterisk reflects significance after FDR ( q = 0.05) correction. (B) When training logistic regression separately for E/I HLP and E+I HLS , we found Alzheimer’s disease showing mostly disruptions in E/I ratio, while ASD and ADHD more prominent changes in E+I density. To illustrate how the relative contribution of E/I HLP and E+I HLS differs across the three disorders, we plotted disorder-specific case-control averages of each biomarker across the frequency spectrum ( C–H ). Asterisks above a trace mark frequency bins in which patient and control means differ significantly (two-sample t -test, FDR-corrected at q = 0.05, performed separately for every disorder-by-biomarker combination).

    Journal: bioRxiv

    Article Title: E/I ratio and net E+I strength are differentially affected across brain disorders

    doi: 10.1101/2025.08.15.670484

    Figure Lengend Snippet: (A) Logistic regression models reveal that patients with Alzheimer’s, ADHD, or ASD can be distinguished from healthy controls by alterations in E/I HLP and E+I HLS . Significance for each disorder was assessed by comparing the average AUC-ROC (black dot) across 50 cross-validation folds against a null distribution generated with 500 random permutations (gray violin plot). Black asterisk reflects significance after FDR ( q = 0.05) correction. (B) When training logistic regression separately for E/I HLP and E+I HLS , we found Alzheimer’s disease showing mostly disruptions in E/I ratio, while ASD and ADHD more prominent changes in E+I density. To illustrate how the relative contribution of E/I HLP and E+I HLS differs across the three disorders, we plotted disorder-specific case-control averages of each biomarker across the frequency spectrum ( C–H ). Asterisks above a trace mark frequency bins in which patient and control means differ significantly (two-sample t -test, FDR-corrected at q = 0.05, performed separately for every disorder-by-biomarker combination).

    Article Snippet: EEG analyses were initially pre-processed using the in-house developed Neurophysiological Biomarker Toolbox (NBT) written in MATLAB.

    Techniques: Biomarker Discovery, Generated, Control