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epi fmri scans  (Philips Healthcare)

 
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    Structured Review

    Philips Healthcare epi fmri scans
    Differential identifiability framework ( I f ). (a) For each subject, two functional connectomes (FC) matrices (restA and restB) were estimated for each half of the <t>fMRI</t> time‐series. (b) FC matrices were vectorized (upper triangular) and placed into a group FC matrix. (c) Principal component analysis (PCA) decomposition was performed on the group FC matrix. Each PC can be arranged as a matrix in the FC domain. (d) Individual FCs were reconstructed using different number of PCs. (e) I diff was estimated for different number of PCs (in order of explained variance) and the number of PCs maximizing I diff found
    Epi Fmri Scans, supplied by Philips 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/epi+fmri+scans/epi+fmri+scans/pmc08249900-92-6-15
    Average 90 stars, based on 1 article reviews
    epi fmri scans - by Bioz Stars, 2026-09
    90/100 stars

    Images

    1) Product Images from "Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease"

    Article Title: Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease

    Journal: Human Brain Mapping

    doi: 10.1002/hbm.25448

    Differential identifiability framework ( I f ). (a) For each subject, two functional connectomes (FC) matrices (restA and restB) were estimated for each half of the fMRI time‐series. (b) FC matrices were vectorized (upper triangular) and placed into a group FC matrix. (c) Principal component analysis (PCA) decomposition was performed on the group FC matrix. Each PC can be arranged as a matrix in the FC domain. (d) Individual FCs were reconstructed using different number of PCs. (e) I diff was estimated for different number of PCs (in order of explained variance) and the number of PCs maximizing I diff found
    Figure Legend Snippet: Differential identifiability framework ( I f ). (a) For each subject, two functional connectomes (FC) matrices (restA and restB) were estimated for each half of the fMRI time‐series. (b) FC matrices were vectorized (upper triangular) and placed into a group FC matrix. (c) Principal component analysis (PCA) decomposition was performed on the group FC matrix. Each PC can be arranged as a matrix in the FC domain. (d) Individual FCs were reconstructed using different number of PCs. (e) I diff was estimated for different number of PCs (in order of explained variance) and the number of PCs maximizing I diff found

    Techniques Used: Functional Assay

    Related Articles

    Functional Assay:

    Article Title: Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease
    Article Snippet: We used T1‐weighted MPRAGE scans and EPI fMRI scans from the initial visit in ADNI2/GO (Philips Platforms, TR/TE = 3000/30 ms, 140 volumes, 3.3 mm thickness, see www.adni-info.org for detailed protocols) for estimation of whole‐brain FCs. fMRI scans were processed with an in‐house MATLAB and FSL based pipeline (Amico et al., ).



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    Differential identifiability framework ( I f ). (a) For each subject, two functional connectomes (FC) matrices (restA and restB) were estimated for each half of the <t>fMRI</t> time‐series. (b) FC matrices were vectorized (upper triangular) and placed into a group FC matrix. (c) Principal component analysis (PCA) decomposition was performed on the group FC matrix. Each PC can be arranged as a matrix in the FC domain. (d) Individual FCs were reconstructed using different number of PCs. (e) I diff was estimated for different number of PCs (in order of explained variance) and the number of PCs maximizing I diff found
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    Image Search Results


    Differential identifiability framework ( I f ). (a) For each subject, two functional connectomes (FC) matrices (restA and restB) were estimated for each half of the fMRI time‐series. (b) FC matrices were vectorized (upper triangular) and placed into a group FC matrix. (c) Principal component analysis (PCA) decomposition was performed on the group FC matrix. Each PC can be arranged as a matrix in the FC domain. (d) Individual FCs were reconstructed using different number of PCs. (e) I diff was estimated for different number of PCs (in order of explained variance) and the number of PCs maximizing I diff found

    Journal: Human Brain Mapping

    Article Title: Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease

    doi: 10.1002/hbm.25448

    Figure Lengend Snippet: Differential identifiability framework ( I f ). (a) For each subject, two functional connectomes (FC) matrices (restA and restB) were estimated for each half of the fMRI time‐series. (b) FC matrices were vectorized (upper triangular) and placed into a group FC matrix. (c) Principal component analysis (PCA) decomposition was performed on the group FC matrix. Each PC can be arranged as a matrix in the FC domain. (d) Individual FCs were reconstructed using different number of PCs. (e) I diff was estimated for different number of PCs (in order of explained variance) and the number of PCs maximizing I diff found

    Article Snippet: We used T1‐weighted MPRAGE scans and EPI fMRI scans from the initial visit in ADNI2/GO (Philips Platforms, TR/TE = 3000/30 ms, 140 volumes, 3.3 mm thickness, see www.adni-info.org for detailed protocols) for estimation of whole‐brain FCs. fMRI scans were processed with an in‐house MATLAB and FSL based pipeline (Amico et al., ).

    Techniques: Functional Assay