intercontact spacing of either 100Âµm, 125Âµm, or 150Âµm. The signal was separated offline with 0 phase shift Butterworth bandpass filter into LFP (0.05 â 200 Hz) ...
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Supporting Information Appendix (SI Appendix) Conscious pattern perception indexed by pupil diameter modulation While the pupil analyses described in the main text were performed on a small subset of “clean” pupil data, we also performed similar analyses on a larger subset of data (1846 trials) across 70 experiments. We determined whether pupil fluctuations were aligned to patterns by calculating the instantaneous phase of the delta band filtered pupil diameter signal during pattern repetitions. Figure S1A shows the average ITC and the normalized amplitude of the pupil signal. When all experiments were included in the comparison, there was no statistically significant difference between the periods before and during pattern repetitions in the ITC or in the normalized analytic amplitude of the pupil signal. However, 15 experiments showed significant pupil ITC during the pattern repetitions but did not show a significant amplitude difference. By examining mean phase consistency at pattern onsets across all experiments (Fig. S1B), we found a significant bias of the pupil signal mean phases at the onsets of R3 and R4 across experiments. The timing of this pattern-locked pupillary modulation resembles the timing of the peak delta ITC values observed during the 3rd and 4th pattern repeats in A1 and thalamic sites. On the wavelet phase histograms, mean phases at pattern repetition onsets are biased towards the negative peak of the pupil diameter signal. This finding is supported by the delta band filtered pupil diameter trace averaged across the selected experiments in Figure S1C. Collectively, the effects observed in this larger dataset concur with the effects observed on the smaller subset of data described in the main text. Both sets of analyses provide evidence that non-human primates consciously perceive patterns and that their pattern detection timing is similar to humans.
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SI Appendix Figure & Figure Legend SI Appendix, Fig. S1. Pattern-related pupil modulation. (A) Top traces display pattern-related ITC of the pupil signal averaged across all experiments (n=70; ITCall, blue) and across experiments where significant pupil ITC was detected during pattern repetitions (n=15; ITCselect, purple). Boxplots to the right show that only the select pupil group exhibits a significant ITC increase (Wilcoxon signed rank, ITCall pre vs during: P=0.7676; ITCsel pre vs during: P=6.1035x10-5). Bottom traces show normalized delta amplitude for the same two groups of experiments (all vs. select). Boxplots show no significant differences in amplitude (Wilcoxon signed rank, Ampall pre vs during: P=0.9045; Ampsel pre vs during: P=0.9780). (B) Mean phase distributions of the pupil diameter signal at pattern repeat onsets for all experiments. Dashed red vertical lines indicate mean phase at each pattern repetition. Green asterisks denote significant phase alignment (n=70, Rayleigh test of uniformity, PP.START=0.4470, PR1=0.9521, PR2=0.1937, PR3=0.0364, PR4=0.0209, PR5=0.1440, PP.END=0.3159). (C) Normalized delta filtered pupil diameter averaged across experiments with significant ITC (ITCsel group in panel (A), n=15).
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SI Appendix Methods Surgical Preparation. Head immobilization was required for accurate pupil measurement. To allow for painless head restraint, socketed Plexiglas bars were custom made, embedded in dental acrylic, and anchored to the skull with orthopedic screws. Cilux recording chambers (Crist Instruments) were implanted and positioned to facilitate orthogonal penetration of the superior temporal gyrus and access to thalamic structures. For a detailed description of complete surgical procedures, see (1). Each animal was given a minimum of 6 weeks for post-operative recovery before being head restrained. Behavioral Preparation. Prior to surgery, each macaque was adapted to a custom fitted primate chair and to the recording chamber. After the post-surgical recovery period, macaques were acclimated to handling and were brought to the laboratory 3 to 4 times per week and trained to allow painless head restraint. Stimulus Presentation. All recordings were performed in a dark, electrically shielded, and soundattenuated chamber. While stimuli were presented, there were no behavioral requirements of the subjects, but eye movement recordings indicate that they remained alert. Given that mechanisms involved in repetitive pattern detection/parsing might be an essential building block of speech learning and perception, we chose a pattern length (588.5 ms) that corresponded to the wavelength of delta frequency band oscillations since delta oscillations also appear to have a role in speech processing (2-4). Although we did not test the frequency specificity of pattern-related entrainment in this study, our lab’s earlier studies on entrainment (5) and other human behavioral results (6) indicate that our observations are likely extendable to at least the theta range. Electrophysiological Recording. All recording areas were initially selected based on stereotaxic coordinate locations (7, 8) and a pre-surgical anatomical MRI obtained for each monkey. The broadband (0.05 – 8 kHz) neuroelectric signal was recorded with a 20 or 44 kHz sampling rate using a linear array multi-electrode (23 equally spaced contacts; Fig. 1B) constructed with an intercontact spacing of either 100µm, 125µm, or 150µm. The signal was separated offline with 0 phase shift Butterworth bandpass filter into LFP (0.05 – 200 Hz) and MUA (300 – 5000 Hz). During each experiment, cortical and thalamic responses to the presentation of pure tones, broadband noise bursts, clicks (Fig. 1B), strobe, and LED flashes were used to determine and refine electrode placement. Longer streams of pseudo-randomly presented pure tones and
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broadband noise bursts, as well as the sound clouds described in the manuscript were used to determine the frequency tuning properties of the recording sites (Fig. 1C). CSD and MUA response properties in all the above paradigms were used to functionally define cortical areas and thalamic nuclei offline. For anatomical verification, a subset of electrode tracks were reconstructed through post-mortem histology following transcardial perfusion and whole brain sectioning (9). Data Analysis and Signal Processing. All offline analyses were conducted using custom-written and industry provided scripts and functions within Matlab (MathWorks, Inc, Massachusetts). By averaging and comparing MUA responses to each auditory stimulus type presented in the current experiments, we deciphered the tuning properties of each recording site (Fig. 1C). To examine response onset differences between the auditory areas we presented clicks with a SOA of 320 ms. Individual onsets were determined by finding the first post-stimulus data point that exceeded the baseline (-30 – 0 ms, pre-stimulus) by 3 times the standard deviation of the baseline and remained significantly different for at least 3 ms. Grouped click onsets were statistically compared using a Bonferroni corrected, Kruskal-Wallis ANOVA with multiple comparisons (Fig. 1E). To inspect whether the pupil size modulation was temporally related to the pattern repetitions (SI Appendix, Fig. S1), the pupil signal was bandpass filtered (1.7 Hz ± 20%) and the instantaneous phase and amplitude was calculated using the Hilbert transform. Trials were excluded if the eyes were closed for more than 30% of the time from -1000 to 3000 ms around R1. ITC and normalized amplitude was averaged across all experiments (n=70) and across selected experiments that showed significant ITC during the R2 to P.END period (n=15). Statistical comparisons for ITC and amplitude were calculated using Wilcoxon signed rank tests. Mean pupil phases across pattern repetitions and across all experiments were calculated and compiled at the onsets of each pattern repeat. Phase distributions were evaluated for significant bias using Rayleigh statistics (SI Appendix, Fig. S1B). The delta-band filtered pupil diameter trace was normalized (by the max amplitude) and averaged across experiments (SI Appendix, Fig. S1C).
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SI Appendix References 1. 2. 3. 4. 5. 6. 7. 8. 9.
Schroeder CE, Mehta AD, & Givre SJ (1998) A spatiotemporal profile of visual system activation revealed by current source density analysis in the awake macaque. Cereb Cortex 8(7):575-592. Ghitza O (2011) Linking speech perception and neurophysiology: speech decoding guided by cascaded oscillators locked to the input rhythm. Frontiers in psychology 2:130. Schroeder CE, Lakatos P, Kajikawa Y, Partan S, & Puce A (2008) Neuronal oscillations and visual amplification of speech. Trends Cogn Sci 12(3):106-113. Giraud AL & Poeppel D (2012) Cortical oscillations and speech processing: emerging computational principles and operations. Nature neuroscience 15(4):511-517. Lakatos P, et al. (2013) The spectrotemporal filter mechanism of auditory selective attention. Neuron 77(4):750-761. Barascud N, Pearce MT, Griffiths TD, Friston KJ, & Chait M (2016) Brain responses in humans reveal ideal observer-like sensitivity to complex acoustic patterns. Proceedings of the National Academy of Sciences of the United States of America 113(5):E616-625. Paxinos G, Huang X, Petrides M, & Toga A (2008) The Rhesus Monkey Brain: in Stereotaxic Coordinates (Academic Press, San Diego, California) 2nd Ed. Saleem KS & Logothetis NK (2012) A Combined MRI and Histology Atlas of the Rhesus Monkey Brain in Stereotaxic Coordinates (Academic Press) 2nd Ed. Schroeder CE, et al. (2001) Somatosensory input to auditory association cortex in the macaque monkey. J Neurophysiol 85(3):1322-1327.