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pca technique  (OriginLab corp)

 
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    OriginLab corp pca technique
    Pca Technique, supplied by OriginLab corp, 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/pca technique/product/OriginLab corp
    Average 90 stars, based on 1 article reviews
    pca technique - by Bioz Stars, 2026-03
    90/100 stars

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    Dynamic functional connectivity and <t>PCA</t> process. (A) Spheres inside the brain show regions of the cognitive control network (blue) and <t>the</t> <t>amygdala</t> (orange). (B) Signals are extracted from all regions, and sliding time windows are created. The figure shows an example of three CC regions with one amygdala region. The orange A represents a value from the amygdala, while the blue numbers 1–3 represent regions from the CC network. (C) Within each window, correlations are computed between each amygdala and all CC regions. These values are entered into a Principal Component Analysis (PCA). (D) Results from each PCA provide scores for participants (right side, in red) and coefficient values for the regions across windows in the dFC (left side in blue). The different shades of colors represent different values. The decreasing saturation in color across the Principal Components (PCs) indicates the amount of variance in dFC they account for, with the first PC having the highest. W = window. dFC = dynamic functional connectivity.
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    Dynamic functional connectivity and <t>PCA</t> process. (A) Spheres inside the brain show regions of the cognitive control network (blue) and <t>the</t> <t>amygdala</t> (orange). (B) Signals are extracted from all regions, and sliding time windows are created. The figure shows an example of three CC regions with one amygdala region. The orange A represents a value from the amygdala, while the blue numbers 1–3 represent regions from the CC network. (C) Within each window, correlations are computed between each amygdala and all CC regions. These values are entered into a Principal Component Analysis (PCA). (D) Results from each PCA provide scores for participants (right side, in red) and coefficient values for the regions across windows in the dFC (left side in blue). The different shades of colors represent different values. The decreasing saturation in color across the Principal Components (PCs) indicates the amount of variance in dFC they account for, with the first PC having the highest. W = window. dFC = dynamic functional connectivity.
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    Dynamic functional connectivity and <t>PCA</t> process. (A) Spheres inside the brain show regions of the cognitive control network (blue) and <t>the</t> <t>amygdala</t> (orange). (B) Signals are extracted from all regions, and sliding time windows are created. The figure shows an example of three CC regions with one amygdala region. The orange A represents a value from the amygdala, while the blue numbers 1–3 represent regions from the CC network. (C) Within each window, correlations are computed between each amygdala and all CC regions. These values are entered into a Principal Component Analysis (PCA). (D) Results from each PCA provide scores for participants (right side, in red) and coefficient values for the regions across windows in the dFC (left side in blue). The different shades of colors represent different values. The decreasing saturation in color across the Principal Components (PCs) indicates the amount of variance in dFC they account for, with the first PC having the highest. W = window. dFC = dynamic functional connectivity.
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    pca technique  (OriginLab corp)
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    Dynamic functional connectivity and <t>PCA</t> process. (A) Spheres inside the brain show regions of the cognitive control network (blue) and <t>the</t> <t>amygdala</t> (orange). (B) Signals are extracted from all regions, and sliding time windows are created. The figure shows an example of three CC regions with one amygdala region. The orange A represents a value from the amygdala, while the blue numbers 1–3 represent regions from the CC network. (C) Within each window, correlations are computed between each amygdala and all CC regions. These values are entered into a Principal Component Analysis (PCA). (D) Results from each PCA provide scores for participants (right side, in red) and coefficient values for the regions across windows in the dFC (left side in blue). The different shades of colors represent different values. The decreasing saturation in color across the Principal Components (PCs) indicates the amount of variance in dFC they account for, with the first PC having the highest. W = window. dFC = dynamic functional connectivity.
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    Dynamic functional connectivity and <t>PCA</t> process. (A) Spheres inside the brain show regions of the cognitive control network (blue) and <t>the</t> <t>amygdala</t> (orange). (B) Signals are extracted from all regions, and sliding time windows are created. The figure shows an example of three CC regions with one amygdala region. The orange A represents a value from the amygdala, while the blue numbers 1–3 represent regions from the CC network. (C) Within each window, correlations are computed between each amygdala and all CC regions. These values are entered into a Principal Component Analysis (PCA). (D) Results from each PCA provide scores for participants (right side, in red) and coefficient values for the regions across windows in the dFC (left side in blue). The different shades of colors represent different values. The decreasing saturation in color across the Principal Components (PCs) indicates the amount of variance in dFC they account for, with the first PC having the highest. W = window. dFC = dynamic functional connectivity.
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    Dynamic functional connectivity and <t>PCA</t> process. (A) Spheres inside the brain show regions of the cognitive control network (blue) and <t>the</t> <t>amygdala</t> (orange). (B) Signals are extracted from all regions, and sliding time windows are created. The figure shows an example of three CC regions with one amygdala region. The orange A represents a value from the amygdala, while the blue numbers 1–3 represent regions from the CC network. (C) Within each window, correlations are computed between each amygdala and all CC regions. These values are entered into a Principal Component Analysis (PCA). (D) Results from each PCA provide scores for participants (right side, in red) and coefficient values for the regions across windows in the dFC (left side in blue). The different shades of colors represent different values. The decreasing saturation in color across the Principal Components (PCs) indicates the amount of variance in dFC they account for, with the first PC having the highest. W = window. dFC = dynamic functional connectivity.
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    Dynamic functional connectivity and <t>PCA</t> process. (A) Spheres inside the brain show regions of the cognitive control network (blue) and <t>the</t> <t>amygdala</t> (orange). (B) Signals are extracted from all regions, and sliding time windows are created. The figure shows an example of three CC regions with one amygdala region. The orange A represents a value from the amygdala, while the blue numbers 1–3 represent regions from the CC network. (C) Within each window, correlations are computed between each amygdala and all CC regions. These values are entered into a Principal Component Analysis (PCA). (D) Results from each PCA provide scores for participants (right side, in red) and coefficient values for the regions across windows in the dFC (left side in blue). The different shades of colors represent different values. The decreasing saturation in color across the Principal Components (PCs) indicates the amount of variance in dFC they account for, with the first PC having the highest. W = window. dFC = dynamic functional connectivity.
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    Dynamic functional connectivity and PCA process. (A) Spheres inside the brain show regions of the cognitive control network (blue) and the amygdala (orange). (B) Signals are extracted from all regions, and sliding time windows are created. The figure shows an example of three CC regions with one amygdala region. The orange A represents a value from the amygdala, while the blue numbers 1–3 represent regions from the CC network. (C) Within each window, correlations are computed between each amygdala and all CC regions. These values are entered into a Principal Component Analysis (PCA). (D) Results from each PCA provide scores for participants (right side, in red) and coefficient values for the regions across windows in the dFC (left side in blue). The different shades of colors represent different values. The decreasing saturation in color across the Principal Components (PCs) indicates the amount of variance in dFC they account for, with the first PC having the highest. W = window. dFC = dynamic functional connectivity.

    Journal: Human Brain Mapping

    Article Title: Dynamic Functional Connectivity Between Amygdala and Cognitive Control Network Predicts Delay Discounting in Older Adolescents

    doi: 10.1002/hbm.70270

    Figure Lengend Snippet: Dynamic functional connectivity and PCA process. (A) Spheres inside the brain show regions of the cognitive control network (blue) and the amygdala (orange). (B) Signals are extracted from all regions, and sliding time windows are created. The figure shows an example of three CC regions with one amygdala region. The orange A represents a value from the amygdala, while the blue numbers 1–3 represent regions from the CC network. (C) Within each window, correlations are computed between each amygdala and all CC regions. These values are entered into a Principal Component Analysis (PCA). (D) Results from each PCA provide scores for participants (right side, in red) and coefficient values for the regions across windows in the dFC (left side in blue). The different shades of colors represent different values. The decreasing saturation in color across the Principal Components (PCs) indicates the amount of variance in dFC they account for, with the first PC having the highest. W = window. dFC = dynamic functional connectivity.

    Article Snippet: A PCA was performed for each amygdala's dFC in MATLAB (2018b).

    Techniques: Functional Assay, Control