Temporal Response Function Research Articles

Neural representation of linguistic feature hierarchy reflects second-language proficiency

Acquiring a new language requires individuals to simultaneously and gradually learn linguistic attributes on multiple levels. Here, we investigated how this learning process changes the neural encoding of natural speech by assessing the encoding of the linguistic feature hierarchy in second-language listeners. Electroencephalography (EEG) signals were recorded from native Mandarin speakers with varied English proficiency and from native English speakers while they listened to audio-stories in English. We measured the temporal response functions (TRFs) for acoustic, phonemic, phonotactic, and semantic features in individual participants and found a main effect of proficiency on linguistic encoding. This effect of second-language proficiency was particularly prominent on the neural encoding of phonemes, showing stronger encoding of “new” phonemic contrasts (i.e., English contrasts that do not exist in Mandarin) with increasing proficiency. Overall, we found that the nonnative listeners with higher proficiency levels had a linguistic feature representation more similar to that of native listeners, which enabled the accurate decoding of language proficiency. This result advances our understanding of the cortical processing of linguistic information in second-language learners and provides an objective measure of language proficiency.

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.

Dithered depth imaging

Single-photon lidar (SPL) is a promising technology for depth measurement at long range or from weak reflectors because of the sensitivity to extremely low light levels. However, constraints on the timing resolution of existing arrays of single-photon avalanche diode (SPAD) detectors limit the precision of resulting depth estimates. In this work, we describe an implementation of subtractively-dithered SPL that can recover high-resolution depth estimates despite the coarse resolution of the detector. Subtractively-dithered measurement is achieved by adding programmable delays into the photon timing circuitry that introduce relative time shifts between the illumination and detection that are shorter than the time bin duration. Careful modeling of the temporal instrument response function leads to an estimator that outperforms the sample mean and results in depth estimates with up to 13 times lower root mean-squared error than if dither were not used. The simple implementation and estimation suggest that globally dithered SPAD arrays could be used for high spatial- and temporal-resolution depth sensing.

The Effect of Stimulus Choice on an EEG-Based Objective Measure of Speech Intelligibility.

Recently, an objective measure of speech intelligibility (SI), based on brain responses derived from the electroencephalogram (EEG), has been developed using isolated Matrix sentences as a stimulus. We investigated whether this objective measure of SI can also be used with natural speech as a stimulus, as this would be beneficial for clinical applications. We recorded the EEG in 19 normal-hearing participants while they listened to two types of stimuli: Matrix sentences and a natural story. Each stimulus was presented at different levels of SI by adding speech weighted noise. SI was assessed in two ways for both stimuli: (1) behaviorally and (2) objectively by reconstructing the speech envelope from the EEG using a linear decoder and correlating it with the acoustic envelope. We also calculated temporal response functions (TRFs) to investigate the temporal characteristics of the brain responses in the EEG channels covering different brain areas. For both stimulus types, the correlation between the speech envelope and the reconstructed envelope increased with increasing SI. In addition, correlations were higher for the natural story than for the Matrix sentences. Similar to the linear decoder analysis, TRF amplitudes increased with increasing SI for both stimuli. Remarkable is that although SI remained unchanged under the no-noise and +2.5 dB SNR conditions, neural speech processing was affected by the addition of this small amount of noise: TRF amplitudes across the entire scalp decreased between 0 and 150 ms, while amplitudes between 150 and 200 ms increased in the presence of noise. TRF latency changes in function of SI appeared to be stimulus specific: the latency of the prominent negative peak in the early responses (50 to 300 ms) increased with increasing SI for the Matrix sentences, but remained unchanged for the natural story. These results show (1) the feasibility of natural speech as a stimulus for the objective measure of SI; (2) that neural tracking of speech is enhanced using a natural story compared to Matrix sentences; and (3) that noise and the stimulus type can change the temporal characteristics of the brain responses. These results might reflect the integration of incoming acoustic features and top-down information, suggesting that the choice of the stimulus has to be considered based on the intended purpose of the measurement.

Robust EEG-Based Decoding of Auditory Attention With High-RMS-Level Speech Segments in Noisy Conditions

The attended speech stream can be detected robustly, even in adverse auditory scenarios with auditory attentional modulation, and can be decoded using electroencephalographic (EEG) data. Speech segmentation based on the relative root-mean-square (RMS) intensity can be used to estimate segmental contributions to perception in noisy conditions. High-RMS-level segments contain crucial information for speech perception. Hence, this study aimed to investigate the effect of high-RMS-level speech segments on auditory attention decoding performance under various signal-to-noise ratio (SNR) conditions. Scalp EEG signals were recorded when subjects listened to the attended speech stream in the mixed speech narrated concurrently by two Mandarin speakers. The temporal response function was used to identify the attended speech from EEG responses of tracking to the temporal envelopes of intact speech and high-RMS-level speech segments alone, respectively. Auditory decoding performance was then analyzed under various SNR conditions by comparing EEG correlations to the attended and ignored speech streams. The accuracy of auditory attention decoding based on the temporal envelope with high-RMS-level speech segments was not inferior to that based on the temporal envelope of intact speech. Cortical activity correlated more strongly with attended than with ignored speech under different SNR conditions. These results suggest that EEG recordings corresponding to high-RMS-level speech segments carry crucial information for the identification and tracking of attended speech in the presence of background noise. This study also showed that with the modulation of auditory attention, attended speech can be decoded more robustly from neural activity than from behavioral measures under a wide range of SNR.

High gamma cortical processing of continuous speech in younger and older listeners

Neural processing along the ascending auditory pathway is often associated with a progressive reduction in characteristic processing rates. For instance, the well-known frequency-following response (FFR) of the auditory midbrain, as measured with electroencephalography (EEG), is dominated by frequencies from ∼100 Hz to several hundred Hz, phase-locking to the acoustic stimulus at those frequencies. In contrast, cortical responses, whether measured by EEG or magnetoencephalography (MEG), are typically characterized by frequencies of a few Hz to a few tens of Hz, time-locking to acoustic envelope features. In this study we investigated a crossover case, cortically generated responses time-locked to continuous speech features at FFR-like rates. Using MEG, we analyzed responses in the high gamma range of 70–200 Hz to continuous speech using neural source-localized reverse correlation and the corresponding temporal response functions (TRFs). Continuous speech stimuli were presented to 40 subjects (17 younger, 23 older adults) with clinically normal hearing and their MEG responses were analyzed in the 70–200 Hz band. Consistent with the relative insensitivity of MEG to many subcortical structures, the spatiotemporal profile of these response components indicated a cortical origin with ∼40 ms peak latency and a right hemisphere bias. TRF analysis was performed using two separate aspects of the speech stimuli: a) the 70–200 Hz carrier of the speech, and b) the 70–200 Hz temporal modulations in the spectral envelope of the speech stimulus. The response was dominantly driven by the envelope modulation, with a much weaker contribution from the carrier. Age-related differences were also analyzed to investigate a reversal previously seen along the ascending auditory pathway, whereby older listeners show weaker midbrain FFR responses than younger listeners, but, paradoxically, have stronger cortical low frequency responses. In contrast to both these earlier results, this study did not find clear age-related differences in high gamma cortical responses to continuous speech. Cortical responses at FFR-like frequencies shared some properties with midbrain responses at the same frequencies and with cortical responses at much lower frequencies.

Dynamic estimation of auditory temporal response functions via state-space models with Gaussian mixture process noise.

Estimating the latent dynamics underlying biological processes is a central problem in computational biology. State-space models with Gaussian statistics are widely used for estimation of such latent dynamics and have been successfully utilized in the analysis of biological data. Gaussian statistics, however, fail to capture several key features of the dynamics of biological processes (e.g., brain dynamics) such as abrupt state changes and exogenous processes that affect the states in a structured fashion. Although Gaussian mixture process noise models have been considered as an alternative to capture such effects, data-driven inference of their parameters is not well-established in the literature. The objective of this paper is to develop efficient algorithms for inferring the parameters of a general class of Gaussian mixture process noise models from noisy and limited observations, and to utilize them in extracting the neural dynamics that underlie auditory processing from magnetoencephalography (MEG) data in a cocktail party setting. We develop an algorithm based on Expectation-Maximization to estimate the process noise parameters from state-space observations. We apply our algorithm to simulated and experimentally-recorded MEG data from auditory experiments in the cocktail party paradigm to estimate the underlying dynamic Temporal Response Functions (TRFs). Our simulation results show that the richer representation of the process noise as a Gaussian mixture significantly improves state estimation and capturing the heterogeneity of the TRF dynamics. Application to MEG data reveals improvements over existing TRF estimation techniques, and provides a reliable alternative to current approaches for probing neural dynamics in a cocktail party scenario, as well as attention decoding in emerging applications such as smart hearing aids. Our proposed methodology provides a framework for efficient inference of Gaussian mixture process noise models, with application to a wide range of biological data with underlying heterogeneous and latent dynamics.

An LMMSE-based Estimation of Temporal Response Function in Auditory Attention Decoding.

One of the remarkable abilities of humans is to focus the attention on a certain speaker in a multi-speaker environment that is known as the cocktail party effect. How the human brain solves this non-trivial task is a challenge that the scientific community has not yet found answers to. In recent years, progress has been made thanks to the development of system identification method based on least-squares (LS) that maps the variations between the cortical signals of a listener and the speech signals present in an auditory scene. Results from numerous two-speaker experiments simulating the cocktail party effect have shown that the auditory attention could be inferred from electroencephalography (EEG) using the LS method. It has been suggested that these methods have the potential to be integrated into hearing aids for algorithmic control. However, a major challenge remains using LS methods such that a large number of scalp EEG electrodes are required in order to get a reliable estimate of the attention. Here we present a new system identification method based on linear minimum mean squared error (LMMSE) that could estimate the attention with the help of two electrodes: one for the true signal estimation and other for the noise estimation. The algorithm is tested using EEG signals collected from ten subjects and its performance is compared against the state-of-the-art LS algorithm.

A Material-Sensing Time-of-Flight Camera

Time-of-flight (ToF) cameras are active imaging sensors able to capture the 3-D geometry of a scene. Objects can be then segmented and recognized from their 3-D geometry. Only very recently it has been shown that ToF cameras can also recognize the material on a per-pixel basis, as opposed to classical computer vision approaches, which rely on texture and operate per patches or regions. Differently, ToF-based material recognition takes profit of differences between the temporal impulse response function of different materials to distinguish them. Existing approaches use either raw data or processed depth measurements at different modulation frequencies to generate distinctive feature vectors for classification. In this letter we stress the importance of the bandlimited character of the material impulse response function (MIRF) and propose direct Fourier sampling. These Fourier coefficients are then used as set of features after appropriate complex scaling. Using orthogonal sensing functions enables classification with fewer but more informative measurements. Minimizing the number of measurements speeds up both acquisition, training, and classification. Moreover, 2-D superpixel segmentation in feature space allows for reliable classification with few queries per region. We demonstrate that the proposed approach allows for real-time simultaneous depth- and material-sensing ToF imaging.

Dissociated modulations of multivoxel activation patterns in the ventral and dorsal visual pathways by the temporal dynamics of stimuli.

IntroductionPrevious studies suggested temporal limitations of visual object identification in the ventral pathway. Moreover, multivoxel pattern analyses (MVPA) of fMRI activation have shown reliable encoding of various object categories including faces and tools in the ventral pathway. By contrast, the dorsal pathway is involved in reaching a target and grasping a tool, and quicker in processing the temporal dynamics of stimulus change. However, little is known about how activation patterns in both pathways may change according to the temporal dynamics of stimulus change.MethodsHere, we measured fMRI responses of two consecutive stimuli with varying interstimulus intervals (ISIs), and we compared how the two visual pathways respond to the dynamics of stimuli by using MVPA and information‐based searchlight mapping.ResultsWe found that the temporal dynamics of stimuli modulate responses of the two visual pathways in opposite directions. Specifically, slower temporal dynamics (longer ISIs) led to greater activity and better MVPA results in the ventral pathway. However, faster temporal dynamics (shorter ISIs) led to greater activity and better MVPA results in the dorsal pathway.ConclusionsThese results are the first to show how temporal dynamics of stimulus change modulated multivoxel fMRI activation pattern change. And such temporal dynamic response function in different ROIs along the two visual pathways may shed lights on understanding functional relationship and organization of these ROIs.

Cortical encoding of melodic expectations in human temporal cortex.

Humans engagement in music rests on underlying elements such as the listeners' cultural background and interest in music. These factors modulate how listeners anticipate musical events, a process inducing instantaneous neural responses as the music confronts these expectations. Measuring such neural correlates would represent a direct window into high-level brain processing. Here we recorded cortical signals as participants listened to Bach melodies. We assessed the relative contributions of acoustic versus melodic components of the music to the neural signal. Melodic features included information on pitch progressions and their tempo, which were extracted from a predictive model of musical structure based on Markov chains. We related the music to brain activity with temporal response functions demonstrating, for the first time, distinct cortical encoding of pitch and note-onset expectations during naturalistic music listening. This encoding was most pronounced at response latencies up to 350 ms, and in both planum temporale and Heschl's gyrus.

Investigation of the resonance of nonwetting droplets in constricted capillary tubes

Fluids in porous media could resonate due to passing seismic waves when meeting certain conditions of excitation frequency and pore geometry. This study of the resonance of fluids can contribute to applications in hydrocarbon microtremor analysis, enhanced oil recovery methods, and environmental remediation of nonaqueous phase liquid contamination. In this study, the analytical expression of the frequency response function and temporal response function is derived to characterize the resonance of two-phase immiscible fluids in the constricted tube. The computational fluid dynamics modeling is used to validate this resonance theory by achieving a good agreement in the prediction of output/input amplification ratio in the frequency and time domains.

Neuro-current response functions: A unified approach to MEG source analysis under the continuous stimuli paradigm

Characterizing the neural dynamics underlying sensory processing is one of the central areas of investigation in systems and cognitive neuroscience. Neuroimaging techniques such as magnetoencephalography (MEG) and Electroencephalography (EEG) have provided significant insights into the neural processing of continuous stimuli, such as speech, thanks to their high temporal resolution. Existing work in the context of auditory processing suggests that certain features of speech, such as the acoustic envelope, can be used as reliable linear predictors of the neural response manifested in M/EEG. The corresponding linear filters are referred to as temporal response functions (TRFs). While the functional roles of specific components of the TRF are well-studied and linked to behavioral attributes such as attention, the cortical origins of the underlying neural processes are not as well understood. In this work, we address this issue by estimating a linear filter representation of cortical sources directly from neuroimaging data in the context of continuous speech processing. To this end, we introduce Neuro-Current Response Functions (NCRFs), a set of linear filters, spatially distributed throughout the cortex, that predict the cortical currents giving rise to the observed ongoing MEG (or EEG) data in response to continuous speech. NCRF estimation is cast within a Bayesian framework, which allows unification of the TRF and source estimation problems, and also facilitates the incorporation of prior information on the structural properties of the NCRFs. To generalize this analysis to M/EEG recordings which lack individual structural magnetic resonance (MR) scans, NCRFs are extended to free-orientation dipoles and a novel regularizing scheme is put forward to lessen reliance on fine-tuned coordinate co-registration. We present a fast estimation algorithm, which we refer to as the Champ-Lasso algorithm, by leveraging recent advances in optimization, and demonstrate its utility through application to simulated and experimentally recorded MEG data under auditory experiments. Our simulation studies reveal significant improvements over existing methods that typically operate in a two-stage fashion, in terms of spatial resolution, response function reconstruction, and recovering dipole orientations. The analysis of experimentally-recorded MEG data without MR scans corroborates existing findings, but also delineates the distinct cortical distribution of the underlying neural processes at high spatiotemporal resolution. In summary, we provide a principled modeling and estimation paradigm for MEG source analysis tailored to extracting the cortical origin of electrophysiological responses to continuous stimuli.

Data-driven spatial filtering for improved measurement of cortical tracking of multiple representations of speech

Objective. Measurement of the cortical tracking of continuous speech from electroencephalography (EEG) recordings using a forward model is an important tool in auditory neuroscience. Usually the stimulus is represented by its temporal envelope. Recently, the phonetic representation of speech was successfully introduced in English. We aim to show that the EEG prediction from phoneme-related speech features is possible in Dutch. The method requires a manual channel selection based on visual inspection or prior knowledge to obtain a summary measure of cortical tracking. We evaluate a method to (1) remove non-stimulus-related activity from the EEG signals to be predicted, and (2) automatically select the channels of interest. Approach. Eighteen participants listened to a Flemish story, while their EEG was recorded. Subject-specific and grand-average temporal response functions were determined between the EEG activity in different frequency bands and several stimulus features: the envelope, spectrogram, phonemes, phonetic features or a combination. The temporal response functions were used to predict EEG from the stimulus, and the predicted was compared with the recorded EEG, yielding a measure of cortical tracking of stimulus features. A spatial filter was calculated based on the generalized eigenvalue decomposition (GEVD), and the effect on EEG prediction accuracy was determined. Main results. A model including both low- and high-level speech representations was able to better predict the brain responses to the speech than a model only including low-level features. The inclusion of a GEVD-based spatial filter in the model increased the prediction accuracy of cortical responses to each speech feature at both single-subject (270% improvement) and group-level (310%). Significance. We showed that the inclusion of acoustical and phonetic speech information and the addition of a data-driven spatial filter allow improved modelling of the relationship between the speech and its brain responses and offer an automatic channel selection.

Where is the cocktail party? Decoding locations of attended and unattended moving sound sources using EEG

Recently, we showed that in a simple acoustic scene with one sound source, auditory cortex tracks the time-varying location of a continuously moving sound. Specifically, we found that both the delta phase and alpha power of the electroencephalogram (EEG) can be used to reconstruct the sound source azimuth. However, in natural settings, we are often presented with a mixture of multiple competing sounds and so we must focus our attention on the relevant source in order to segregate it from the competing sources e.g. ‘cocktail party effect’. While many studies have examined this phenomenon in the context of sound envelope tracking by the cortex, it is unclear how we process and utilize spatial information in complex acoustic scenes with multiple sound sources.To test this, we created an experiment where subjects listened to two concurrent sound stimuli that were moving within the horizontal plane over headphones while we recorded their EEG. Participants were tasked with paying attention to one of the two presented stimuli. The data were analyzed by deriving linear mappings, temporal response functions (TRF), between EEG data and attended as well unattended sound source trajectories. Next, we used these TRFs to reconstruct both trajectories from previously unseen EEG data. In a first experiment we used noise stimuli and included the task involved spatially localizing embedded targets. Then, in a second experiment, we employed speech stimuli and a non-spatial speech comprehension task.Results showed the trajectory of an attended sound source can be reliably reconstructed from both delta phase and alpha power of EEG even in the presence of distracting stimuli. Moreover, the reconstruction was robust to task and stimulus type. The cortical representation of the unattended source position was below detection level for the noise stimuli, but we observed weak tracking of the unattended source location for the speech stimuli by the delta phase of EEG. In addition, we demonstrated that the trajectory reconstruction method can in principle be used to decode selective attention on a single-trial basis, however, its performance was inferior to envelope-based decoders. These results suggest a possible dissociation of delta phase and alpha power of EEG in the context of sound trajectory tracking. Moreover, the demonstrated ability to localize and determine the attended speaker in complex acoustic environments is particularly relevant for cognitively controlled hearing devices.

Impact of deuteration on the ultrafast nonlinear optical response of toluene and nitrobenzene.

Nonlinear pulse propagation inside highly nonlinear media requires accurate knowledge on the temporal response function of the materials used particular in the case of liquids. Here we study the impact of deuteration on the ultrafast dynamics of toluene and nitrobenzene via all optical Kerr gating, showing substantially different electronic and molecular contributions, which was quantified by fitting a multichannel decay model to the data points. Specifically we found that deuteration imposes the time-integrated nonlinearities to reduce particular for toluene which could be caused by both reduced electronic hyperpolarizabilities as well as weaker intermolecular interactions. The results achieved reveal that deuterated organic solvents represent promising materials for infrared photonics since they offer extended infrared transmission compared to their non-deuterated counterparts while maintained strong nonlinear responses.

Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research

A common problem in neural recordings is the low signal-to-noise ratio (SNR), particularly when using non-invasive techniques like magneto- or electroencephalography (M/EEG). To address this problem, experimental designs often include repeated trials, which are then averaged to improve the SNR or to infer statistics that can be used in the design of a denoising spatial filter. However, collecting enough repeated trials is often impractical and even impossible in some paradigms, while analyses on existing data sets may be hampered when these do not contain such repeated trials. Therefore, we present a data-driven method that takes advantage of the knowledge of the presented stimulus, to achieve a joint noise reduction and dimensionality reduction without the need for repeated trials. The method first estimates the stimulus-driven neural response using the given stimulus, which is then used to find a set of spatial filters that maximize the SNR based on a generalized eigenvalue decomposition. As the method is fully data-driven, the dimensionality reduction enables researchers to perform their analyses without having to rely on their knowledge of brain regions of interest, which increases accuracy and reduces the human factor in the results. In the context of neural tracking of a speech stimulus using EEG, our method resulted in more accurate short-term temporal response function (TRF) estimates, higher correlations between predicted and actual neural responses, and higher attention decoding accuracies compared to existing TRF-based decoding methods. We also provide an extensive discussion on the central role played by the generalized eigenvalue decomposition in various denoising methods in the literature, and address the conceptual similarities and differences with our proposed method.

Quantifying the individual auditory and visual brain response in 7-month-old infants watching a brief cartoon movie

Electroencephalography (EEG) continues to be the most popular method to investigate cognitive brain mechanisms in young children and infants. Most infant studies rely on the well-established and easy-to-use event-related brain potential (ERP). As a severe disadvantage, ERP computation requires a large number of repetitions of items from the same stimulus-category, compromising both ERPs’ reliability and their ecological validity in infant research. We here explore a way to investigate infant continuous EEG responses to an ongoing, engaging signal (i.e., “neural tracking”) by using multivariate temporal response functions (mTRFs), an approach increasingly popular in adult EEG research. N = 52 infants watched a 5-min episode of an age-appropriate cartoon while the EEG signal was recorded. We estimated and validated forward encoding models of auditory-envelope and visual-motion features. We compared individual and group-based (‘generic’) models of the infant brain response to comparison data from N = 28 adults. The generic model yielded clearly defined response functions for both, the auditory and the motion regressor. Importantly, this response profile was present also on an individual level, albeit with lower precision of the estimate but above-chance predictive accuracy for the modelled individual brain responses. In sum, we demonstrate that mTRFs are a feasible way of analyzing continuous EEG responses in infants. We observe robust response estimates both across and within participants from only 5 min of recorded EEG signal. Our results open ways for incorporating more engaging and more ecologically valid stimulus materials when probing cognitive, perceptual, and affective processes in infants and young children.

Selective spatial attention involves two alpha-band components associated with distinct spatiotemporal and functional characteristics

Attention is crucial for efficiently coordinating resources over multiple objects in a visual scene. Recently, a growing number of studies suggest that attention is implemented through a temporal organization process during which resources are dynamically allocated over a multitude of objects, yet the associated neural evidence, particularly in low-level sensory areas, is still limited. Here we used EEG recordings in combination with a temporal response function (TRF) approach to examine the spatiotemporal characteristics of neuronal impulse response in covert selective attention. We demonstrate two distinct alpha-band components – one in post-central parietal area and one in contralateral occipital area – that are involved in coordinating neural representations of attended and unattended stimuli. Specifically, consistent with previous findings, the central alpha-band component showed enhanced activities for unattended versus attended stimuli within the first 200 ms temporal lag of TRF response, suggesting its inhibitory function in attention. In contrast, the contralateral occipital component displayed relatively earlier activation for the attended than unattended one in the TRF response. Furthermore, the central component but not the occipital component was correlated with attentional behavioral performance. Finally, the parietal area exerted directional influences on the occipital activity through alpha-band rhythm. Taken together, spatial attention involves two hierarchically organized alpha-band components that are associated with distinct spatiotemporal characteristics and presumably play different functions.

Cortical Tracking of Complex Sound Envelopes: Modeling the Changes in Response with Intensity.

Characterizing how the brain responds to stimuli has been a goal of sensory neuroscience for decades. One key approach has been to fit linear models to describe the relationship between sensory inputs and neural responses. This has included models aimed at predicting spike trains, local field potentials, BOLD responses, and EEG/MEG. In the case of EEG/MEG, one explicit use of this linear modeling approach has been the fitting of so-called temporal response functions (TRFs). TRFs have been used to study how auditory cortex tracks the amplitude envelope of acoustic stimuli, including continuous speech. However, such linear models typically assume that variations in the amplitude of the stimulus feature (i.e., the envelope) produce variations in the magnitude but not the latency or morphology of the resulting neural response. Here, we show that by amplitude binning the stimulus envelope, and then using it to fit a multivariate TRF, we can better account for these amplitude-dependent changes, and that this leads to a significant improvement in model performance for both amplitude-modulated noise and continuous speech in humans. We also show that this performance can be further improved through the inclusion of an additional envelope representation that emphasizes onsets and positive changes in the stimulus, consistent with the idea that while some neurons track the entire envelope, others respond preferentially to onsets in the stimulus. We contend that these results have practical implications for researchers interested in modeling brain responses to amplitude modulated sounds.