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pearson correlation coefficient matlab function  (MathWorks Inc)


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    MathWorks Inc pearson correlation coefficient matlab function
    Pearson Correlation Coefficient Matlab Function, 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/product/pearson+correlation+function/pm38662736-278-9-12
    Average 90 stars, based on 1 article reviews
    pearson correlation coefficient matlab function - by Bioz Stars, 2026-09
    90/100 stars

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    Article Title: Slow and fast cortical cholinergic arousal is reduced in a mouse model of focal seizures with impaired consciousness
    Article Snippet: The relationships between the processed signals were then calculated using the Pearson correlation coefficient (r) with MATLAB (see and ).

    Article Title: Vegetation monitoring in mining area using canopy reflectance and landsat8 oli image
    Article Snippet: From the above figure, the linear correlation is not so obvious that it is not easy to make a judgment, and the general view is that the correlation is weak, so the next operation of the correlation coefficient is indispensable. (2) Calculation of Pearson correlation coefficient and significance by MATLAB The correlation coefficient between CSVI of 12 images and CSVI of hyperspectral image obtained in MATLAB is shown in Table 4.

    Article Title: Noninvasive estimation of internal spinal alignment in patients with adolescent idiopathic scoliosis using PCdare and back shape asymmetry
    Article Snippet: The Pearson correlation coefficient (corrcoef MATLAB function) was calculated between asymmetry indices and rCAs in an upright standing position manually annotated by a board-certified spine surgeon in PACS (MERLIN Diagnostic Workcenter, Version 7.1, Phönix-PACS GmbH, Freiburg im Breisgau, Germany).

    Article Title: Harmful algal bloom prediction using empirical dynamic modeling.
    Article Snippet: • Chlorophyll-a concentration is a key in-

    Article Title: Dynamics of ammonia-oxidizing microorganisms in gradient temperature-regulated reactors under low ammonia loading condition.
    Article Snippet: Ammonia-oxidizing microorganisms (AOMs) play a crucial role in nitrogen removal in engineered systems.. However, temperature fluctuations can lead to ammonia oxidation failure.. This study investigated the effects of a broad temperature range (10.5, 14, 17.5, 21, 26.4, 35, 38.5, 42, and 45.5 C) on the distribution of ammoniaoxidizing archaea (AOA), ammonia-oxidizing bacteria (AOB), and comammox together with Nitrospira and Nitrobacter in gradient temperature-regulated reactors operated under low ammonia loading condition.

    Article Title: Bridging circuit modeling and signal analysis to understand the risk of crosstalk contamination in brain recordings
    Article Snippet: Signal correlation, defined as a normalized measure of the similarity of two time-dependent signals, was calculated as the Pearson correlation coefficient (function corrcoef in MATLAB).

    Article Title: Noninvasive estimation of internal spinal alignment in patients with adolescent idiopathic scoliosis using PCdare and back shape asymmetry.
    Article Snippet: Comparison with clinical gold standard The Pearson correlation coefficient (corrcoef MATLAB function) was calculated between asymmetry indices and rCAs in an upright standing position manually annotated by a board-certified spine surgeon in PACS (MERLIN Diagnostic Workcenter, Version 7.1, Phönix-PACS GmbH, Freiburg im Breisgau, Germany).

    Article Title: Slow and fast cortical cholinergic arousal is reduced in a mouse model of focal seizures with impaired consciousness.
    Article Snippet: Article Slow and fast cortical chol inergic arousal is reduced in a mouse model of focal seizures with impaired consciousness

    Diagnostic Assay:

    Article Title: Slow and fast cortical cholinergic arousal is reduced in a mouse model of focal seizures with impaired consciousness
    Article Snippet: The relationships between the processed signals were then calculated using the Pearson correlation coefficient (r) with MATLAB (see and ).

    Article Title: Vegetation monitoring in mining area using canopy reflectance and landsat8 oli image
    Article Snippet: From the above figure, the linear correlation is not so obvious that it is not easy to make a judgment, and the general view is that the correlation is weak, so the next operation of the correlation coefficient is indispensable. (2) Calculation of Pearson correlation coefficient and significance by MATLAB The correlation coefficient between CSVI of 12 images and CSVI of hyperspectral image obtained in MATLAB is shown in Table 4.

    Article Title: Noninvasive estimation of internal spinal alignment in patients with adolescent idiopathic scoliosis using PCdare and back shape asymmetry
    Article Snippet: The Pearson correlation coefficient (corrcoef MATLAB function) was calculated between asymmetry indices and rCAs in an upright standing position manually annotated by a board-certified spine surgeon in PACS (MERLIN Diagnostic Workcenter, Version 7.1, Phönix-PACS GmbH, Freiburg im Breisgau, Germany).

    Article Title: Harmful algal bloom prediction using empirical dynamic modeling.
    Article Snippet: • Chlorophyll-a concentration is a key in-

    Article Title: Dynamics of ammonia-oxidizing microorganisms in gradient temperature-regulated reactors under low ammonia loading condition.
    Article Snippet: Ammonia-oxidizing microorganisms (AOMs) play a crucial role in nitrogen removal in engineered systems.. However, temperature fluctuations can lead to ammonia oxidation failure.. This study investigated the effects of a broad temperature range (10.5, 14, 17.5, 21, 26.4, 35, 38.5, 42, and 45.5 C) on the distribution of ammoniaoxidizing archaea (AOA), ammonia-oxidizing bacteria (AOB), and comammox together with Nitrospira and Nitrobacter in gradient temperature-regulated reactors operated under low ammonia loading condition.

    Article Title: Bridging circuit modeling and signal analysis to understand the risk of crosstalk contamination in brain recordings
    Article Snippet: Signal correlation, defined as a normalized measure of the similarity of two time-dependent signals, was calculated as the Pearson correlation coefficient (function corrcoef in MATLAB).

    Article Title: Noninvasive estimation of internal spinal alignment in patients with adolescent idiopathic scoliosis using PCdare and back shape asymmetry.
    Article Snippet: Comparison with clinical gold standard The Pearson correlation coefficient (corrcoef MATLAB function) was calculated between asymmetry indices and rCAs in an upright standing position manually annotated by a board-certified spine surgeon in PACS (MERLIN Diagnostic Workcenter, Version 7.1, Phönix-PACS GmbH, Freiburg im Breisgau, Germany).

    Article Title: Slow and fast cortical cholinergic arousal is reduced in a mouse model of focal seizures with impaired consciousness.
    Article Snippet: Article Slow and fast cortical chol inergic arousal is reduced in a mouse model of focal seizures with impaired consciousness



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    MathWorks Inc pearson correlations matlab function corr
    a Venn diagrams depicting the percent of the total pseudopopulation in each region that was selective for the sample location around the poke initiating each task phase. Overlapping circles in the Venn diagram represent the percent of neurons that share retrospective sample location selectivity around either two or all three task phase pokes. b Example GLM-derived beta-weight histograms from significant sample location-encoding neurons in the GLM showing how most neurons in the MOs remain selective for the same port across sample and delay pokes (top), but neurons in the vmPFC switch initial sample location selectivity from sample to delay (bottom). c <t>Pearson</t> beta-weight correlations quantifying the similarity in location selectivity between pokes initiating one task phase, to pokes initiating another one. MOs location selectivity is similar from sample to delay pokes, but destabilizes over the course of the task. dmPFC remains the most stable across time. vmPFC shifts location selectivity from the sample to delay pokes, but then stabilizes later in the task. Asterisks represent an r value that is significantly different from zero, while Orth ( Orth ogonal) represents an r value that is not significantly different from zero. The reason for the non-significant r value from the vmPFC correlation in the Delay Poke vs Late Delay condition is because of the much smaller number of selective neurons in vmPFC during this time. Numbers above each bar indicate the count of significantly selective neurons in each region used for the correlation.
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    MathWorks Inc pearson correlation coefficient matlab function
    a Venn diagrams depicting the percent of the total pseudopopulation in each region that was selective for the sample location around the poke initiating each task phase. Overlapping circles in the Venn diagram represent the percent of neurons that share retrospective sample location selectivity around either two or all three task phase pokes. b Example GLM-derived beta-weight histograms from significant sample location-encoding neurons in the GLM showing how most neurons in the MOs remain selective for the same port across sample and delay pokes (top), but neurons in the vmPFC switch initial sample location selectivity from sample to delay (bottom). c <t>Pearson</t> beta-weight correlations quantifying the similarity in location selectivity between pokes initiating one task phase, to pokes initiating another one. MOs location selectivity is similar from sample to delay pokes, but destabilizes over the course of the task. dmPFC remains the most stable across time. vmPFC shifts location selectivity from the sample to delay pokes, but then stabilizes later in the task. Asterisks represent an r value that is significantly different from zero, while Orth ( Orth ogonal) represents an r value that is not significantly different from zero. The reason for the non-significant r value from the vmPFC correlation in the Delay Poke vs Late Delay condition is because of the much smaller number of selective neurons in vmPFC during this time. Numbers above each bar indicate the count of significantly selective neurons in each region used for the correlation.
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    MathWorks Inc pearson’s linear correlation function
    a Venn diagrams depicting the percent of the total pseudopopulation in each region that was selective for the sample location around the poke initiating each task phase. Overlapping circles in the Venn diagram represent the percent of neurons that share retrospective sample location selectivity around either two or all three task phase pokes. b Example GLM-derived beta-weight histograms from significant sample location-encoding neurons in the GLM showing how most neurons in the MOs remain selective for the same port across sample and delay pokes (top), but neurons in the vmPFC switch initial sample location selectivity from sample to delay (bottom). c <t>Pearson</t> beta-weight correlations quantifying the similarity in location selectivity between pokes initiating one task phase, to pokes initiating another one. MOs location selectivity is similar from sample to delay pokes, but destabilizes over the course of the task. dmPFC remains the most stable across time. vmPFC shifts location selectivity from the sample to delay pokes, but then stabilizes later in the task. Asterisks represent an r value that is significantly different from zero, while Orth ( Orth ogonal) represents an r value that is not significantly different from zero. The reason for the non-significant r value from the vmPFC correlation in the Delay Poke vs Late Delay condition is because of the much smaller number of selective neurons in vmPFC during this time. Numbers above each bar indicate the count of significantly selective neurons in each region used for the correlation.
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    Image Search Results


    a Venn diagrams depicting the percent of the total pseudopopulation in each region that was selective for the sample location around the poke initiating each task phase. Overlapping circles in the Venn diagram represent the percent of neurons that share retrospective sample location selectivity around either two or all three task phase pokes. b Example GLM-derived beta-weight histograms from significant sample location-encoding neurons in the GLM showing how most neurons in the MOs remain selective for the same port across sample and delay pokes (top), but neurons in the vmPFC switch initial sample location selectivity from sample to delay (bottom). c Pearson beta-weight correlations quantifying the similarity in location selectivity between pokes initiating one task phase, to pokes initiating another one. MOs location selectivity is similar from sample to delay pokes, but destabilizes over the course of the task. dmPFC remains the most stable across time. vmPFC shifts location selectivity from the sample to delay pokes, but then stabilizes later in the task. Asterisks represent an r value that is significantly different from zero, while Orth ( Orth ogonal) represents an r value that is not significantly different from zero. The reason for the non-significant r value from the vmPFC correlation in the Delay Poke vs Late Delay condition is because of the much smaller number of selective neurons in vmPFC during this time. Numbers above each bar indicate the count of significantly selective neurons in each region used for the correlation.

    Journal: Communications Biology

    Article Title: Divergent subregional information processing in mouse prefrontal cortex during working memory

    doi: 10.1038/s42003-024-06926-8

    Figure Lengend Snippet: a Venn diagrams depicting the percent of the total pseudopopulation in each region that was selective for the sample location around the poke initiating each task phase. Overlapping circles in the Venn diagram represent the percent of neurons that share retrospective sample location selectivity around either two or all three task phase pokes. b Example GLM-derived beta-weight histograms from significant sample location-encoding neurons in the GLM showing how most neurons in the MOs remain selective for the same port across sample and delay pokes (top), but neurons in the vmPFC switch initial sample location selectivity from sample to delay (bottom). c Pearson beta-weight correlations quantifying the similarity in location selectivity between pokes initiating one task phase, to pokes initiating another one. MOs location selectivity is similar from sample to delay pokes, but destabilizes over the course of the task. dmPFC remains the most stable across time. vmPFC shifts location selectivity from the sample to delay pokes, but then stabilizes later in the task. Asterisks represent an r value that is significantly different from zero, while Orth ( Orth ogonal) represents an r value that is not significantly different from zero. The reason for the non-significant r value from the vmPFC correlation in the Delay Poke vs Late Delay condition is because of the much smaller number of selective neurons in vmPFC during this time. Numbers above each bar indicate the count of significantly selective neurons in each region used for the correlation.

    Article Snippet: We quantified how these beta-weights changed over time using Pearson correlations ( r , MATLAB function corr ) comparing the significant beta-weight population vector at one time point to the beta-weight population of those same neurons at a future time point.

    Techniques: Derivative Assay