Journal: bioRxiv

Article Title: Top-down feedback enables flexible coding strategies in olfactory cortex

doi: 10.1101/2021.08.06.455459

Figure Lengend Snippet: (A). Firings of piriform cells (PCs) in response to a representative odor with centrifugal feedback OFF. PCs fire with a transient burst of spikes that are sharply truncated by local FBIs and followed by persistent suppression. Population firing rates: (mean ± SD, n = 10 trials). (B). Similar to (A) but for centrifugal feedback ON. Persistent dynamics of PCs arise due to centrifugal feedback. FBI cells are only sparsely activated. (C). PC population firing rate in response to all model odors (mean ± SD, n = 300 odors). The traces of feedback OFF and ON are normalized to have the same amplitude of peak. (D). Schematic illustration to quantify the dynamics of the PC population firing rate in response to a single odor ( n = 300 odors). Peak: the first peak in the trial-averaged population firing rate ( n = 10 trials). Slope: the slope of a linear function (oblique dashed line) fitted to the mean firing rate between the Peak and the first time when it drops below baseline (firing rate preceding the activation of the earliest glomerulus). Delay: the latency between the Peak and the activation time of earliest glomerulus defined by the odor (vertical dashed line with a triangle on top). (E). Comparison of PC dynamics between centrifugal feedback OFF vs. ON. (E1). Peak firing rate. Error bar: ±SD (*** p < 0.001 Wilcoxon signed rank test). Connecting lines for 30 example odors are shown. (E2). Similar to E1 but for Slope. (E3). Histogram of the Delay across 300 odors. Top: M/T cells; bottom: PCs. Centrifugal feedback reduces the responses latency of PCs without affecting that of M/T cells.

Article Snippet: Statistical tests for significance were performed with a two-sided Wilcoxon rank sum test (ranksum function in MATLAB) when samples were independent ( and ) and with a two-sided Wilcoxon signed rank test (signrank function in MATLAB) for paired samples ( and ).

Techniques: Activation Assay

Journal: bioRxiv

Article Title: Top-down feedback enables flexible coding strategies in olfactory cortex

doi: 10.1101/2021.08.06.455459

Figure Lengend Snippet: (A). Piriform cell (PC) responses to Odor-1 and Odor-2 with centrifugal feedback OFF. Top and middle: raster plot of piriform cell responses to Odor-1 and Odor-2 in an example trial. Bottom: population firing rates of PCs responding to two odors (mean ± SD, n = 10 trials). The firing rate separations between Odor-1 and Odor-2 are nonsignificant (ns: p > 0.05 Wilcoxon rank-sum test). (B). Similar to (A) but for centrifugal feedback ON (mean ±SD, n = 10 trials). The firings of PCs are persistent throughout successive glomerular activation of both odors. The firing rate between Odor-1 and Odor-2 are significant different during 70 – 90 ms and 120 – 140 ms (** p < 0.01 Wilcoxon rank-sum test). (C). Low-dimensional projections of ensemble trajectories of PCs onto the first three principal components when centrifugal feedback is OFF. Each trace corresponds to a single-trial PC response to one of the odors (color coded). Trajectories for different odors are tangled and non-separable. (D). Similar to (C) but for centrifugal feedback ON. Centrifugal feedback pushes apart the low-dimensional trajectories evoked by different odors and makes them more separable.

Article Snippet: Statistical tests for significance were performed with a two-sided Wilcoxon rank sum test (ranksum function in MATLAB) when samples were independent ( and ) and with a two-sided Wilcoxon signed rank test (signrank function in MATLAB) for paired samples ( and ).

Techniques: Activation Assay

Journal: bioRxiv

Article Title: Confidence over competence: Real-time integration of social information in human continuous perceptual decision-making

doi: 10.1101/2024.08.19.608609

Figure Lengend Snippet: (a) Metacognitive sensitivity of joystick response for one example subject. Left: Distribution of joystick accuracy for low (gray) vs high eccentricity stimulus states (colored, median split). Accuracy and eccentricity were averaged for the last 30 frames (250 ms) prior to a stimulus direction change. Coherence is color-coded. Right: Corresponding receiver-operating characteristics (‘ROC’) between the two distributions for each coherence level (color-coded). A ROC curve along the diagonal would indicate similar accuracy distributions between hits and misses, suggesting no metacognitive sensitivity. (b) Population AUC values (black dots) are consistently above 0.5 (p<0.001, Two-sided Wilcoxon signed rank test for distribution with median 0.5), demonstrating that high eccentricity was more often associated with high accuracy, suggesting metacognitive-sensitive confidence readouts.

Article Snippet: Differences between experimental conditions were tested with a two-sided Wilcoxon signed rank test (Matlab: signrank, for paired samples) and a two-sided paired Wilcoxon rank sum test (Matlab: ranksum).

Techniques:

Journal: bioRxiv

Article Title: Confidence over competence: Real-time integration of social information in human continuous perceptual decision-making

doi: 10.1101/2024.08.19.608609

Figure Lengend Snippet: (a) Reward score in dyadic vs solo sessions. Scores were averaged across all targets, including misses. All solo and (human-human) dyadic sessions were pooled within-subject. Inset: coherence-wise averaging of reward scores. Stimulus coherence is color-coded, see legend on the right in panel c. Each subject contributes one data point per stimulus coherence level. The median score across all subjects for each coherence condition is overlaid in brighter color hues. Error bars show 99% confidence intervals of the median in solo and dyadic conditions. (b) Social modulation between solo and dyadic experiments, measured as AUC, for joystick accuracy (top) and eccentricity (bottom, Wilcoxon rank sum test, Bonferroni-corrected) of individual participants. Coherence was pooled within-subjects. See for average performance in dyadic experiments and for three examples of social modulation and how the AUC captures the directionality of the behavioral change. (c) Coherence dependent modulation of hit rate (top row), accuracy (center row) and eccentricity (bottom row) in dyadic vs solo setting. First column: quantification of dyadic vs solo change in behavior for each participant and coherence condition. All dyadic and solo sessions were pooled, respectively. AUC was used to quantify the direction and magnitude of social modulation. A value of 0.5 corresponds to perfect overlap between solo and dyadic response distributions, 1 and 0 imply perfect separation between experimental conditions (see ). Gray lines correspond to the AUC of individual participants. Red shading illustrates the 99% confidence intervals of the mean across participants. Bold, black lines show the mean across the population. Data were averaged within-subject first, before pooling across coherence conditions. Second column: average social modulation displayed for different solo performance quartiles. Coherence is color-coded. The grouping into quartiles was done for each response corresponding dimension separately. Please see for comparison of raw solo joystick responses with social modulation. See for quartile grouping across response dimensions. Third column: statistical comparison between joystick accuracy and eccentricity in solo and dyadic experiments, for each coherence condition. Sessions were pooled according to experimental condition within-subject. The percentage of participants with significantly different distribution in solo and dyadic sessions is displayed (Wilcoxon rank sum test, Bonferroni- corrected). The directionality of the significant effect in each subject was established with the AUC shown in the first column.

Article Snippet: Differences between experimental conditions were tested with a two-sided Wilcoxon signed rank test (Matlab: signrank, for paired samples) and a two-sided paired Wilcoxon rank sum test (Matlab: ranksum).

Techniques: Comparison

Journal: Communications Biology

Article Title: Human cortical folding across regions within individual brains follows universal scaling law

doi: 10.1038/s42003-019-0421-7

Figure Lengend Snippet: Slope and offset over age for Alzheimer’s subjects. a Slope estimates based on different lobes of the same hemisphere (box plots) for Alzheimer’s patients (AD) and controls. As a reference the horizontal grey line indicates the predicted 1.25 slope. To enable comparison, we also show the group-based slope estimates as grey error bars. b Offset (assuming 1.25 as slope) is shown for AD and control groups for different lobes. Solid line indicates the mean, and shaded area shows the 95% bootstrapped confidence intervals for the mean. c Effect size of offset ( K Lobe ) from the control vs. AD between-group comparisons at different lobes. Empty circles show where the effect was significant ( p < 0.05) under a two-sided ranksum test

Article Snippet: Statistical significance between the Control and AD group were determined using the two-sided ranksum test in Matlab.

Techniques: Comparison, Control