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pca technique  (MathWorks Inc)
90
MathWorks Inc pca technique
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.
Pca Technique, 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
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pca technique  (RStudio)
90
RStudio pca technique
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.
Pca Technique, supplied by RStudio, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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pca technique - by Bioz Stars, 2026-03
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pca technique  (Metax GmbH)
90
Metax GmbH pca technique
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.
Pca Technique, supplied by Metax GmbH, 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/Metax GmbH
Average 90 stars, based on 1 article reviews
pca technique - by Bioz Stars, 2026-03
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pca technique  (OriginLab corp)
90
OriginLab corp pca technique
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.
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
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pca technique  (GraphPad Software Inc)
90
GraphPad Software Inc pca technique
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.
Pca Technique, supplied by GraphPad Software Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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pca technique - by Bioz Stars, 2026-03
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data reduction using pca technique  (MathWorks Inc)
90
MathWorks Inc data reduction using pca technique
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.
Data Reduction Using Pca Technique, 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
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Average 90 stars, based on 1 article reviews
data reduction using pca technique - by Bioz Stars, 2026-03
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pca technique  (Respiratory Motion)
90
Respiratory Motion pca technique
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.
Pca Technique, supplied by Respiratory Motion, 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/Respiratory Motion
Average 90 stars, based on 1 article reviews
pca technique - by Bioz Stars, 2026-03
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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