pearson correlation function in  (MathWorks Inc)

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

Journal: bioRxiv

Article Title: Large-scale deep tissue voltage imaging with targeted illumination confocal microscopy

doi: 10.1101/2023.07.21.548930

Figure Lengend Snippet: (a-c) Example Voltron2 fluorescence images under targeted illumination with confocal slit width set to 4.5, 22.5, and 156 µm. Scale bar 50 µm. (d) Voltron2 fluorescence image over the same FOV but acquired without targeted illumination and with a confocal slit width of 156 µm. TI, targeted illumination. Scale bar 50 µm. (e,h,i) Comparison of spike Δ F / F , spike detection fidelity d ′ , and spike SNR measured with targeted illumination and confocal slit widths of 4.5, 11.3, 22.5, and 156 µm (n = 30 cells from 6 FOVs, 2 mice). Box plots: box, 25th (Q1, bottom line) to 75 th (Q3, top line) percentiles; whiskers, Q 1 − 1.5 × I Q R to Q 3 + 1.5 × I Q R , where I Q R = Q 3 − Q 1 ; middle line, median (m); notch, from m − 1.57 × I Q R / n to m + 1.57 × I Q R / n ; dots, measurement points. p < 0.05, p < 0.01, p < 0.001, no label if p ≥ 0.05, pairwise Wilcoxon signed-rank test, see for statistics. (f,g,j) Comparison of spike Δ F / F , photobleaching rate, and spike SNR measured with and without targeted illumination when using a 14 µm confocal slit. For (f,j), n = 19 cells from 5 FOVs, 2 mice. For (g), n = 92 cells from 5 FOVs, 2 mice. (l,m,n) Example images (scale bar, 20 µm) and corresponding fluorescence traces from two neighboring neurons with targeted illumination and confocal slit widths of 4.5, 22.5, and 156 µm (from top to bottom). Gray line, fluorescence traces; red line, extracted subthreshold Vm traces; r, Pearson cross-correlation coefficient between the subthreshold Vm traces from the 2 neurons.

Article Snippet: To analyze Vm-Vm correlations, we calculated Pearson cross-correlation coefficients (Matlab function corrcoef ) for the extracted subthreshold traces F s u b t from pairs of neurons.

Techniques: Fluorescence, Comparison, IF-P