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original code for ocf  (MathWorks Inc)


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    MathWorks Inc original code for ocf
    Each panel in the top row shows a true 20 × 2 weight matrix W = [ w 1 , w 2 ] (as well as its transpose, two small rectangles) and corresponding eigenconnectivity matrix B (large squares). The four panels differ in relative strength c ∈ [0, 1] of intra-module network variability, as indicated at bottom of figure. c = 0 implies no variability within each module and greater c indicates the stronger intra-module variability; in particular, c = 0.6 implies a greater intra-module variability than the inter-module. Bottom three rows show corresponding estimates B <t>by</t> <t>PCA,</t> <t>OCF,</t> and MCF (proposed method), displayed in the same manner. In all panels, red, white, and blue indicate positive, zero, and negative values, respectively (except for weight matrix rectangles in PCA, which are left blank). Each panel and each W and B were scaled individually so that maximum absolute value corresponds to boundary of color range (displayed at bottom right).
    Original Code For Ocf, supplied by MathWorks Inc, 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/result/original code for ocf/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    original code for ocf - by Bioz Stars, 2026-03
    90/100 stars

    Images

    1) Product Images from "Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis"

    Article Title: Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis

    Journal: PLoS ONE

    doi: 10.1371/journal.pone.0168180

    Each panel in the top row shows a true 20 × 2 weight matrix W = [ w 1 , w 2 ] (as well as its transpose, two small rectangles) and corresponding eigenconnectivity matrix B (large squares). The four panels differ in relative strength c ∈ [0, 1] of intra-module network variability, as indicated at bottom of figure. c = 0 implies no variability within each module and greater c indicates the stronger intra-module variability; in particular, c = 0.6 implies a greater intra-module variability than the inter-module. Bottom three rows show corresponding estimates B by PCA, OCF, and MCF (proposed method), displayed in the same manner. In all panels, red, white, and blue indicate positive, zero, and negative values, respectively (except for weight matrix rectangles in PCA, which are left blank). Each panel and each W and B were scaled individually so that maximum absolute value corresponds to boundary of color range (displayed at bottom right).
    Figure Legend Snippet: Each panel in the top row shows a true 20 × 2 weight matrix W = [ w 1 , w 2 ] (as well as its transpose, two small rectangles) and corresponding eigenconnectivity matrix B (large squares). The four panels differ in relative strength c ∈ [0, 1] of intra-module network variability, as indicated at bottom of figure. c = 0 implies no variability within each module and greater c indicates the stronger intra-module variability; in particular, c = 0.6 implies a greater intra-module variability than the inter-module. Bottom three rows show corresponding estimates B by PCA, OCF, and MCF (proposed method), displayed in the same manner. In all panels, red, white, and blue indicate positive, zero, and negative values, respectively (except for weight matrix rectangles in PCA, which are left blank). Each panel and each W and B were scaled individually so that maximum absolute value corresponds to boundary of color range (displayed at bottom right).

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    original code for ocf  (MathWorks Inc)
    90
    MathWorks Inc original code for ocf
    Each panel in the top row shows a true 20 × 2 weight matrix W = [ w 1 , w 2 ] (as well as its transpose, two small rectangles) and corresponding eigenconnectivity matrix B (large squares). The four panels differ in relative strength c ∈ [0, 1] of intra-module network variability, as indicated at bottom of figure. c = 0 implies no variability within each module and greater c indicates the stronger intra-module variability; in particular, c = 0.6 implies a greater intra-module variability than the inter-module. Bottom three rows show corresponding estimates B <t>by</t> <t>PCA,</t> <t>OCF,</t> and MCF (proposed method), displayed in the same manner. In all panels, red, white, and blue indicate positive, zero, and negative values, respectively (except for weight matrix rectangles in PCA, which are left blank). Each panel and each W and B were scaled individually so that maximum absolute value corresponds to boundary of color range (displayed at bottom right).
    Original Code For Ocf, supplied by MathWorks Inc, 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/result/original code for ocf/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    original code for ocf - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Each panel in the top row shows a true 20 × 2 weight matrix W = [ w 1 , w 2 ] (as well as its transpose, two small rectangles) and corresponding eigenconnectivity matrix B (large squares). The four panels differ in relative strength c ∈ [0, 1] of intra-module network variability, as indicated at bottom of figure. c = 0 implies no variability within each module and greater c indicates the stronger intra-module variability; in particular, c = 0.6 implies a greater intra-module variability than the inter-module. Bottom three rows show corresponding estimates B by PCA, OCF, and MCF (proposed method), displayed in the same manner. In all panels, red, white, and blue indicate positive, zero, and negative values, respectively (except for weight matrix rectangles in PCA, which are left blank). Each panel and each W and B were scaled individually so that maximum absolute value corresponds to boundary of color range (displayed at bottom right).

    Journal: PLoS ONE

    Article Title: Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis

    doi: 10.1371/journal.pone.0168180

    Figure Lengend Snippet: Each panel in the top row shows a true 20 × 2 weight matrix W = [ w 1 , w 2 ] (as well as its transpose, two small rectangles) and corresponding eigenconnectivity matrix B (large squares). The four panels differ in relative strength c ∈ [0, 1] of intra-module network variability, as indicated at bottom of figure. c = 0 implies no variability within each module and greater c indicates the stronger intra-module variability; in particular, c = 0.6 implies a greater intra-module variability than the inter-module. Bottom three rows show corresponding estimates B by PCA, OCF, and MCF (proposed method), displayed in the same manner. In all panels, red, white, and blue indicate positive, zero, and negative values, respectively (except for weight matrix rectangles in PCA, which are left blank). Each panel and each W and B were scaled individually so that maximum absolute value corresponds to boundary of color range (displayed at bottom right).

    Article Snippet: We implemented all the methods in Matlab; we used eigs function for PCA and the original code for OCF ( https://www.cs.helsinki.fi/u/ahyvarin/code/ocf/ ).

    Techniques: