Journal: The Journal of Neuroscience

Article Title: Levodopa-Induced Dyskinesia Is Strongly Associated with Resonant Cortical Oscillations

doi: 10.1523/JNEUROSCI.3047-12.2012

Figure Lengend Snippet: Cortical oscillations associated with dyskinesia are tuned to 80 Hz. A, Example of power trend analyses of resonant oscillations during dyskinesia. Top, Signal-to-(pink)noise ratio (SNR) in the γ80 band following levodopa injection at 0 min (SNR >3 dB shown in bold). Bottom, Peak oscillation frequency as a function of time (red, exponential function fitted to the 30 min following oscillation onset, defined as SNR >3 dB; fitting period indicated in bold; goodness-of-fit, 0.02 Hz2). B, Exponential functions fitted to 20 different recordings, aligned to oscillation onset (goodness-of-fit, 0.03 ± 0.01 Hz2); note the very similar time-frequency relation in all experiments. C, Coupling strength between LFP signals recorded from different electrodes in the motor cortex of the lesioned hemisphere as estimated from measures of coherence in two different frequency bands, plotted as a function of electrode separation (OFF and ON levodopa in black and red, respectively). Note the significantly increased LFP coupling strength in the 80 Hz band (γ80, left) in the dyskinetic state (rOFF = −0.21, rON = −0.11, p ≪ 0.001, two-sample Kolmogorov–Smirnov goodness-of-fit hypothesis test). Right, The θ band (4–12 Hz) included as a reference; in contrast to the γ80, θ coupling was stronger in the non-dyskinetic state (rOFF = −0.14, rON = −0.18, p < 0.001). Calculations were performed for a 5 min period during baseline and dyskinesia, respectively. The median (solid line) and the 25th and 75th percentile (shaded area) are shown for all recordings (n = 18).

Article Snippet: Parameters in different groups, e.g., referring to baseline and dyskinesia, were compared using the two-sample Kolmogorov–Smirnov test (kstest2 function in Matlab, MathWorks) or the two-sided Mann–Whitney U test (ranksum function in Matlab).

Techniques: Injection

Journal: bioRxiv

Article Title: Local interneurons in the murine visual thalamus have diverse receptive fields and can provide feature selective inhibition to relay cells

doi: 10.1101/2023.08.10.549394

Figure Lengend Snippet: (A) 1σ contours of 2D Gaussian fits to the receptive fields of interneurons (green ellipses) and relay cells (black ellipses) aligned to the center of the stimulus grid (interneurons, n = 27; relay cells, n = 205). Fits were made to the full extent of each (sub)region for On, Off, On-Off cells or to the centers for center-surround cells. (B) Leftmost pair of box and violin plots show that the distributions of receptive field sizes for interneurons (green) and relay cells (black) are statistically indistinguishable; Wilcoxon rank-sum test and two-sample Kolmogorov-Smirnov test (p>0.05). For box plots, horizontal lines indicate the mean, circles denote the median, and vertical lines the SD; for violin plots, horizontal lines are individual values. Remaining plots show distributions of receptive field sizes for individual classes of cell: On, Off, On-Off, SBC, and Adaptive (see for an illustration of adaptive cells).

Article Snippet: For non-parametric datasets, we used the Wilcoxon rank sum test (MATLAB function ‘ranksum’) in addition to the two-sample Kolmogorov-Smirnov test (MATLAB function ‘kstest2’).

Techniques:

Journal: bioRxiv

Article Title: Nanopore Sequencing at Mars, Europa and Microgravity Conditions

doi: 10.1101/2020.01.09.899716

Figure Lengend Snippet: a RMS vibration distributions for ground and flight. b Nanopore translocation time as measured by alignment of ionic current to the genomic reference: distribution for <10 ms. Ground (blue), flight (light brown), both (dark brown).

Article Snippet: A Kolmogorov-Smirnov test was performed with the MATLAB kstest2() function on the base translocation times for ground vs. flight.

Techniques: Translocation Assay