Human Auditory cortical processing of changes in interaural correlation

Sensitivity to the similarity of the acoustic waveforms at the two ears, and specifically to changes in similarity, is crucial to auditory scene analysis and extraction of objects from background. Here, we use the high temporal resolution of magnetoencephalography to investigate the dynamics of cortical processing of changes in interaural correlation, a measure of interaural similarity, and compare them with behavior. Stimuli are interaurally correlated or uncorrelated wideband noise, immediately followed by the same noise with intermediate degrees of interaural correlation. Behaviorally, listeners' sensitivity to changes in interaural correlation is asymmetrical. Listeners are faster and better at detecting transitions from correlated noise than transitions from uncorrelated noise. The cortical response to the change in correlation is characterized by an activation sequence starting from ∼50 ms after change. The strength of this response parallels behavioral performance: auditory cortical mechanisms are much less sensitive to transitions from uncorrelated noise than from correlated noise. In each case, sensitivity increases with interaural correlation difference. Brain responses to transitions from uncorrelated noise lag those from correlated noise by ∼80 ms, which may be the neural correlate of the observed behavioral response time differences. Importantly, we demonstrate differences in location and time course of neural processing: transitions from correlated noise are processed by a distinct neural population, and with greater speed, than transitions from uncorrelated noise

Processing asymmetry of transitions between order and disorder in human auditory cortex

Purpose: To develop an algorithm to resolve intrinsic problems with dose calculations using pencil beams when particles involved in each beam are overreaching a lateral density interface or when they are detouring in a laterally heterogeneous medium. Method and Materials: A finding on a Gaussian distribution, such that it can be approximately decomposed into multiple narrower, shifted, and scaled ones, was applied to dynamic splitting of pencil beams implemented in a dose calculation algorithm for proton and ion beams. The method was tested in an experiment with a range-compensated carbon-ion beam. Its effectiveness and efficiency were evaluated for carbon-ion and proton beams in a heterogeneous phantom model. Results: The splitting dose calculation reproduced the detour effect observed in the experiment, which amounted to about 10% at a maximum or as large as the lateral particle-disequilibrium effect. The proton-beam dose generally showed large scattering effects including the overreach and detour effects. The overall computational times were 9 s and 45 s for non-splitting and splitting carbon-ion beams and 15 s and 66 s for non-splitting and splitting proton beams. Conclusions: The beam-splitting method was developed and verified to resolve the intrinsic size limitation of the Gaussian pencil-beam model in dose calculation algorithms. The computational speed slowed down by factor of 5, which would be tolerable for dose accuracy improvement at a maximum of 10%, in our test case.AAPM Annual Meeting 200

The anticipation of events in time

Humans anticipate events signaled by sensory cues. It is commonly assumed that two uncertainty parameters modulate the brain's capacity to predict: the hazard rate (HR) of event probability and the uncertainty in time estimation which increases with elapsed time. We investigate both assumptions by presenting event probability density functions (PDFs) in each of three sensory modalities. We show that perceptual systems use the reciprocal PDF and not the HR to model event probability density. We also demonstrate that temporal uncertainty does not necessarily grow with elapsed time but can also diminish, depending on the event PDF. Previous research identified neuronal activity related to event probability in multiple levels of the cortical hierarchy (sensory (V4), association (LIP), motor and other areas) proposing the HR as an elementary neuronal computation. Our results—consistent across vision, audition, and somatosensation—suggest that the neurobiological implementation of event anticipation is based on a different, simpler and more stable computation than HR: the reciprocal PDF of events in time

Exploring morphological correlations among H2CO, 12CO, MSX and continuum mappings

There are relatively few H2CO mappings of large-area giant molecular cloud (GMCs). H2CO absorption lines are good tracers for low-temperature molecular clouds towards star formation regions. Thus, the aim of the study was to identify H2CO distributions in ambient molecular clouds. We investigated morphologic relations among 6-cm continuum brightness temperature (CBT) data and H2CO (111-110; Nanshan 25-m radio telescope), 12CO (1--0; 1.2-m CfA telescope) and midcourse space experiment (MSX) data, and considered the impact of background components on foreground clouds. We report simultaneous 6-cm H2CO absorption lines and H110\alpha radio recombination line observations and give several large-area mappings at 4.8 GHz toward W49 (50'\times50'), W3 (70'\times90'), DR21/W75 (60'\times90') and NGC2024/NGC2023 (50'\times100') GMCs. By superimposing H2CO and 12CO contours onto the MSX color map, we can compare correlations. The resolution for H2CO, 12CO and MSX data was about 10', 8' and 18.3", respectively. Comparison of H2CO and 12CO contours, 8.28-\mu m MSX colorscale and CBT data revealed great morphological correlation in the large area, although there are some discrepancies between 12CO and H2CO peaks in small areas. The NGC2024/NGC2023 GMC is a large area of HII regions with a high CBT, but a H2CO cloud to the north is possible against the cosmic microwave background. A statistical diagram shows that 85.21% of H2CO absorption lines are distributed in the intensity range from -1.0 to 0 Jy and the \Delta V range from 1.206 to 5 km/s.Comment: 18 pages, 22 figures, 5 tables. Accepted to be published in Astrophysics and Space Scienc

The impact of phase entrainment on auditory detection is highly variable: Revisiting a key finding

Ample evidence shows that the human brain carefully tracks acoustic temporal regularities in the input, perhaps by entraining cortical neural oscillations to the rate of the stimulation. To what extent the entrained oscillatory activity influences processing of upcoming auditory events remains debated. Here, we revisit a critical finding from Hickok et al. (2015) that demonstrated a clear impact of auditory entrainment on subsequent auditory detection. Participants were asked to detect tones embedded in stationary noise, following a noise that was amplitude modulated at 3 Hz. Tonal targets occurred at various phases relative to the preceding noise modulation. The original study (N = 5) showed that the detectability of the tones (presented at near-threshold intensity) fluctuated cyclically at the same rate as the preceding noise modulation. We conducted an exact replication of the original paradigm (N = 23) and a conceptual replication using a shorter experimental procedure (N = 24). Neither experiment revealed significant entrainment effects at the group level. A restricted analysis on the subset of participants (36%) who did show the entrainment effect revealed no consistent phase alignment between detection facilitation and the preceding rhythmic modulation. Interestingly, both experiments showed group-wide presence of a non-cyclic behavioural pattern, wherein participants' detection of the tonal targets was lower at early and late time points of the target period. The two experiments highlight both the sensitivity of the task to elicit oscillatory entrainment and the striking individual variability in performance

Acoustically driven cortical delta oscillations underpin prosodic chunking

Oscillation-based models of speech perception postulate a cortical computational principle by which decoding is performed within a window structure derived by a segmentation process. Segmentation of syllable-size chunks is realized by a theta oscillator. We provide evidence for an analogous role of a delta oscillator in the segmentation of phrase-sized chunks. We recorded Magnetoencephalography (MEG) in humans, while participants performed a target identification task. Random-digit strings, with phrase-long chunks of two digits, were presented at chunk rates of 1.8 Hz or 2.6 Hz, inside or outside the delta frequency band (defined here to be 0.5 - 2 Hz). Strong periodicities were elicited by chunk rates inside of delta in superior, middle temporal areas and speech-motor integration areas. Periodicities were diminished or absent for chunk rates outside delta, in line with behavioral performance. Our findings show that prosodic chunking of phrase-sized acoustic segments is correlated with acoustic-driven delta oscillations, expressing anatomically specific patterns of neuronal periodicities

Neural signatures of temporal anticipation in human cortex represent event probability density

Temporal prediction is a fundamental function of neural systems. Recent results show that humans anticipate future events by calculating probability density functions, rather than hazard rates. However, direct neural evidence for this hypothesized mechanism is lacking. We recorded neural activity using magnetoencephalography as participants anticipated auditory and visual events distributed in time. We show that temporal anticipation, measured as reaction times, approximates the event probability density function, but not hazard rate. Temporal anticipation manifests as spatiotemporally patterned activity in three anatomically and functionally distinct parieto-temporal and sensorimotor cortical areas. Each of these areas revealed a marked neural signature of anticipation: Prior to sensory cues, activity in a specific frequency range of neural oscillations, spanning alpha and beta ranges, encodes the event probability density function. These neural signals predicted reaction times to imminent sensory cues. These results demonstrate that supra-modal representations of probability density across cortex underlie the anticipation of future events

Two attentive strategies reducing subjective distortions in serial duration perception

Humans tend to perceptually distort (dilate/shrink) the duration of brief stimuli presented in a sequence when discriminating the duration of a second stimulus (Comparison) from the duration of a first stimulus (Standard). This type of distortion, termed “Time order error” (TOE), is an important window into the determinants of subjective perception. We hypothesized that stimulus durations would be optimally processed, suppressing subjective distortions in serial perception, if the events to be compared fell within the boundaries of rhythmic attentive sampling (4–8 Hz, theta band). We used a two-interval forced choice (2IFC) experimental design, and in three separate experiments tested different Standard durations: 120-ms, corresponding to an 8.33 Hz rhythmic attentive window; 160 ms, corresponding to a 6.25 Hz window; and 200 ms, for a 5 Hz window. We found that TOE, as measured by the Constant Error metric, is sizeable for a 120-ms Standard, is reduced for a 160-ms Standard, and statistically disappears for 200-ms Standard events, confirming our hypothesis. For 120- and 160-ms Standard events, to reduce TOEs it was necessary to increase the interval between the Standard and the Comparison event from sub-second (400, 800 ms) to supra-second (1600, 2000 ms) lags, suggesting that the orienting of attention in time waiting for the Comparison event to onset may work as a back-up strategy to optimize its encoding. Our results highlight the flexible use of two different attentive strategies to optimize subjective time perception

Modulation spectra capture EEG responses to speech signals and drive distinct temporal response functions

Speech signals have a unique shape of long-term modulation spectrum that is distinct from environmental noise, music, and non-speech vocalizations. Does the human auditory system adapt to the speech long-term modulation spectrum and efficiently extract critical information from speech signals? To answer this question, we tested whether neural responses to speech signals can be captured by specific modulation spectra of non-speech acoustic stimuli. We generated amplitude modulated (AM) noise with the speech modulation spectrum and 1/f modulation spectra of different exponents to imitate temporal dynamics of different natural sounds. We presented these AM stimuli and a 10-min piece of natural speech to 19 human participants undergoing electroencephalography (EEG) recording. We derived temporal response functions (TRFs) to the AM stimuli of different spectrum shapes and found distinct neural dynamics for each type of TRFs. We then used the TRFs of AM stimuli to predict neural responses to the speech signals, and found that (1) the TRFs of AM modulation spectra of exponents 1, 1.5, and 2 preferably captured EEG responses to speech signals in the δ band and (2) the θ neural band of speech neural responses can be captured by the AM stimuli of an exponent of 0.75. Our results suggest that the human auditory system shows specificity to the long-term modulation spectrum and is equipped with characteristic neural algorithms tailored to extract critical acoustic information from speech signals