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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).
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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: