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pearson’s linear correlation function  (MathWorks Inc)


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    MathWorks Inc pearson’s linear correlation function
    Pearson’s Linear Correlation 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/pm37783930-112-9-14
    Average 90 stars, based on 1 article reviews
    pearson’s linear correlation function - by Bioz Stars, 2026-09
    90/100 stars

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    Injection:

    Article Title: Oxidative potential of urban PM 2.5 in relation to chemical composition: Importance of fossil driven sources.
    Article Snippet: Pearson’s correlation between DTTv and chemical components was calculated using MATLAB, and significance was considered at p < 0.05.

    Article Title: Distribution of spine classes shows intra-neuronal dendritic heterogeneity in mouse cortex
    Article Snippet: We used Pearson’s chi-square test (Matlab’s function crosstab) to test for significant differences in class distribution on dendrites ( ) with a 5% significance level and corrected for multiple comparisons with the Bonferroni correction.

    Article Title: Decoupling of motor cortex to movement in Parkinson’s dyskinesia rescued by sub-anaesthetic ketamine
    Article Snippet: Second, Pearson’s correlations between all pairwise neuron combinations were computed (corr, MATLAB).

    Article Title: Enclosing 95% confidence area and volume to center of pressure and center of mass in posturography using optimization algorithm and coordinate ascent methods.
    Article Snippet: Balance control has been evaluated using center of pressure (CoP) and center of mass (CoM).. One of the most common approaches in stabilometry is enclosing ellipse to 95% of data using principal component analysis (PCA) or covariance methods.. However, these methods have limitations, including normality assumption, lack of accuracy, and sample size influence.

    Article Title: Distribution of spine classes shows intra-neuronal dendritic heterogeneity in mouse cortex
    Article Snippet: We used Pearson’s chi-square test (Matlab’s function crosstab) to test for significant differences in class distribution on dendrites (Fig. 2) with a 5% significance level and corrected for multiple comparisons with the Bonferroni correction.

    Article Title: Differences in cerebral structure among patients with amnestic mild cognitive impairment and patients with Alzheimer’s disease
    Article Snippet: By using MATLAB, the Pearson correlation analysis between the values of the significantly different brain areas obtained from SBM/VBM and MMSE/MoCA scores was performed, with the age and gender of individuals used as concomitant variables.

    Article Title: Subtle alteration in transcriptional memory governs the lineage-level cell cycle duration heterogeneities of mammalian cells
    Article Snippet: Using the Pearson correlation function in MATLAB, we calculated the correlation in these lineage pairs (M-D, D-D, and C-C).



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    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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    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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    pearson’s linear correlation function  (MathWorks Inc)
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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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    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