Biology subjects

Simon, J. Z.

Publications and source records attributed to Simon, J. Z..

At least 19 recordsLinked to original sources

bioRxiv2026

Neurophysiological Functional Connectivity Changes during Difficult Listening in Older and Younger Adults

Older adults often report increased difficulty understanding speech in noisy listening environments. These difficulties are thought to arise from neurophysiological changes associated with aging, including at the level of cortex. Speech comprehension is believed to rely on coordinated activity across distributed cortical regions, mediated by the directional flow of neural signals - their functional connectivity. This connectivity can be analyzed within specific frequency bands, e.g., Delta and Theta, each thought to play a distinct role in speech and language processing. Here, we utilize the Network Localized Granger Causality (NLGC) framework applied to magnetoencephalography (MEG) data to simultaneously estimate cortical source current activities and the their directed functional connectivity. Whole-brain connectivity graphs were analyzed using graph-cluster enhancement and cluster testing. Temporal Response Functions (TRFs) were obtained by relating NLGC-derived neural current estimates to the speech stimulus presented to listeners. The band-specific networks we identify are consistent with the canonical "dual-stream" model of the auditory processing pathway. Delta-band networks primarily involve temporofrontal connections, whereas Theta-band networks showed stronger temporoparietal connectivity. These cortical networks are shown to evolve as listening conditions become increasingly adverse, incorporating frontoparietal areas associated with attention orienting and control. We find that older adults exhibit increased connectivity within areas of the ventral and dorsal auditory streams across all listening conditions, suggesting increased cortical recruitment to support speech comprehension. Although older adults exhibit enhanced time-locked responses to the speech stimulus, these responses did not significantly coincide with their connectivity patterns (and similarly for younger adults), indicating that their connectivity increases are not explained by central gain mechanisms. Together, our findings demonstrate age- and condition-dependent reorganization of speech-related cortical connectivity across neurophysiological frequency bands.

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bioRxiv2026

Very Early Cortical Auditory Responses to Speech in Humans

A foundational measure of the auditory systems ability to process sound correctly is the auditory brainstem response (ABR) and middle latency response (MLR) complex, but their dependence on stimulus complexity and behavioral relevance is poorly understood. Here we recorded noninvasive, simultaneous electroencephalography (EEG) and magnetoencephalography (MEG) in adults listening to both naturalistic speech and clicks, and computed the resulting temporal response functions (TRFs). The TRFs ABR-MLR complex for speech listening contains a very early cortical peak (11 ms latency), which has not been well-characterized, and is absent for click stimuli. Other significant differences in latency and source between the speech and click MLR complexes point to further differences in their auditory processing, in thalamus and cortex. In contrast, the speech and click TRFs ABR complex both share a similar, prominent wave V response. In summation, the use of naturalistic speech reveals how early auditory responses reflect its ethological relevance.

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bioRxiv2025

Neural recordings of continuous speech reveal robust signatures of prediction in second language learners of English

When listening to speech in their native language, speakers use prior context to anticipate upcoming phonemes, words, and concepts, integrating information at the sublexical, lexical, and sentence level. While it has been suggested that late second language learners do not predict to the same extent as native listeners, adequately evaluating this claim requires measurement of predictions at these multiple levels of representation simultaneously in natural speech. We recorded magnetoencephalography (MEG) responses from native Mandarin and Sinhala speakers listening to continuous narrative English speech. We used multivariate temporal response function (mTRF) analysis to investigate whether second language listeners demonstrate the same markers of prediction in neural data as native English speakers listening to the same stimuli. We demonstrate that late second language listeners exhibit strikingly similar responses to native speakers in sensitivity to phoneme surprisal and entropy with respect to sublexical, lexical, and sentence-level context. The few small response differences we observed appear most likely to arise from specific properties of the native languages, rather than general differences between native and second-language listening. These results provide evidence that late second-language listeners indeed leverage prediction in similar ways as native listeners in understanding continuous speech. This suggests that multivariate analyses of neural data from naturalistic listening may be vital in carefully evaluating the differences and similarities in speech prediction across populations. Significance StatementMuch is still unknown about how people listening to a second language predict upcoming words and sounds. Here, we leverage neuroimaging during continuous speech and analyze responses to multiple speech language features in the signal to study the neural encoding of prediction simultaneously at multiple levels of linguistic context. We observe robust encoding of statistical properties tied to prediction at all context levels in second-language learners of English and that responses are strikingly similar between native and second language listeners. Speech language features are encoded similarly in both groups of language learners, with few differences between the native and second language listeners, indicating that second language listeners predict upcoming input similarly to native listeners.

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bioRxiv2025

Neural and Behavioral Changes in Older Adults from Auditory-Cognitive Training

Speech perception in noisy environments is a common challenge among older adults, even for those with clinically normal hearing. Cognitive decline may be one of the contributing factors, and, as such, auditory-cognitive training may enhance speech perception in these conditions. This study aims to determine if auditory-cognitive training can improve speech-in-noise listening in normal-hearing, older adults using neural and behavioral measures, supplemented with comparisons across younger and older adults. Neural responses were obtained using magnetoencephalography (MEG) while participants listened to long, narrative passages (60 s) under four noise conditions. Neural measures employed reverse correlation using encoding and decoding models, via the temporal response function (TRF) framework, to predict neural responses and reconstruct stimulus features, respectively, with the boosting algorithm to enforce sparsity. Behavioral measures, such as working memory (reading span; RSPAN), speech perception in noise (SPIN), and nonlinguistic auditory stream segregation (stochastic figure-ground; SFG) showed improvement post-training, along with neural and subjective ratings for listening effort. Additionally, auditory-cognitive training may enhance the neural contrast between the selectively attended and unattended stimulus reconstructions, and pre-training SFG performance may predict the extent of this neuroplasticity change. These results provide promising, additional insight into the effects of auditory-cognitive training, both perceptually and neurally.

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bioRxiv2024

Neural Dynamics of the Processing of Speech Features: Evidence for a Progression of Features from Acoustic to Sentential Processing

When we listen to speech, our brains neurophysiological responses "track" its acoustic features, but it is less well understood how these auditory responses are enhanced by linguistic content. Here, we recorded magnetoencephalography (MEG) responses while subjects listened to four types of continuous-speech-like passages: speech-envelope modulated noise, English-like non-words, scrambled words, and a narrative passage. Temporal response function (TRF) analysis provides strong neural evidence for the emergent features of speech processing in cortex, from acoustics to higher-level linguistics, as incremental steps in neural speech processing. Critically, we show a stepwise hierarchical progression of progressively higher order features over time, reflected in both bottom-up (early) and top-down (late) processing stages. Linguistically driven top-down mechanisms take the form of late N400-like responses, suggesting a central role of predictive coding mechanisms at multiple levels. As expected, the neural processing of lower-level acoustic feature responses is bilateral or right lateralized, with left lateralization emerging only for lexical-semantic features. Finally, our results identify potential neural markers, linguistic level late responses, derived from TRF components modulated by linguistic content, suggesting that these markers are indicative of speech comprehension rather than mere speech perception. Significance StatementWe investigate neural processing mechanisms as speech evolves from acoustic signals to meaningful language, using stimuli ranging from without any linguistic information to fully well-formed linguistic content. Computational models based on speech and linguistic hierarchy reveal that cortical responses time-lock to emergent features from acoustics to linguistic processes at the sentence level, with increasing the semantic information in the acoustic input. Temporal response functions (TRFs) uncovered millisecond-level processing dynamics as speech and language stages unfold. Each speech feature undergoes early and late processing stages, with the former driven by bottom-up activation and the latter influenced by top-down mechanisms. These insights enhance our understanding of the hierarchical nature of auditory language processing.

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bioRxiv2023

Cortical Responses Time-Locked to Continuous Speech in the High-Gamma Band Depend on Selective Attention

Auditory cortical responses to speech obtained by magnetoencephalography (MEG) show robust speech tracking to the speakers fundamental frequency in the high-gamma band (70-200 Hz), but little is currently known about whether such responses depend on the focus of selective attention. In this study 22 human subjects listened to concurrent, fixed-rate, speech from male and female speakers, and were asked to selectively attend to one speaker at a time, while their neural responses were recorded with MEG. The male speakers pitch range coincided with the lower range of the high-gamma band, whereas the female speakers higher pitch range had much less overlap, and only at the upper end of the high-gamma band. Neural responses were analyzed using the temporal response function (TRF) framework. As expected, the responses demonstrate robust speech tracking of the fundamental frequency in the high-gamma band, but only to the males speech, with a peak latency of approximately 40 ms. Critically, the response magnitude depends on selective attention: the response to the male speech is significantly greater when male speech is attended than when it is not attended, under acoustically identical conditions. This is a clear demonstration that even very early cortical auditory responses are influenced by top-down, cognitive, neural processing mechanisms.

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bioRxiv2023

Changes in Cortical Directional Connectivity during Difficult Listening in Younger and Older Adults

One way to investigate the mechanisms that underlie speech comprehension under difficult listening conditions is via cortical connectivity. The innovative Network Localized Granger Causality (NLGC) framework was applied to magnetoencephalography (MEG) data, obtained from older and younger subjects performing a speech listening task in noisy conditions, in delta and theta frequency bands. Directional connectivity between frontal, temporal, and parietal lobes was analyzed. Both aging- and condition-related changes were found, particularly in theta. In younger adults, as background noise increased, there was a transition from predominantly temporal-to-frontal (bottom-up) connections, to predominantly frontal-to-temporal (top-down). In contrast, older adults showed bidirectional information flow between frontal and temporal cortices even for speech in quiet, not changing substantially with increased noise. Additionally, younger listeners did not show changes in the nature of their cortical links for different listening conditions, whereas older listeners exhibited a switch from predominantly facilitative links to predominantly sharpening, when noise increased. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=169 HEIGHT=200 SRC="FIGDIR/small/541500v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@174c132org.highwire.dtl.DTLVardef@37c5a5org.highwire.dtl.DTLVardef@1910e38org.highwire.dtl.DTLVardef@3c94e0_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIDifferent bands show strong differences in directional functional connectivity patterns C_LIO_LIDirectional functional connectivity patterns altered by listening task difficulty C_LIO_LIAging dramatically alters directional functional connectivity patterns in during listening C_LIO_LINature of functional connectivity, additive vs subtractive, depends on age and task C_LI

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bioRxiv2023

Neural Tracking Measures of Speech Intelligibility: Manipulating Intelligibility while Keeping Acoustics Unchanged

Neural speech tracking has advanced our understanding of how our brains rapidly map an acoustic speech signal onto linguistic representations and ultimately meaning. It remains unclear, however, how speech intelligibility is related to the corresponding neural responses. Many studies addressing this question vary the level of intelligibility by manipulating the acoustic waveform, but this makes it difficult to cleanly disentangle effects of intelligibility from underlying acoustical confounds. Here, using magnetoencephalography (MEG) recordings, we study neural measures of speech intelligibility by manipulating intelligibility while keeping the acoustics strictly unchanged. Acoustically identical degraded speech stimuli (three-band noise vocoded, [~]20 s duration) are presented twice, but the second presentation is preceded by the original (non-degraded) version of the speech. This intermediate priming, which generates a pop-out percept, substantially improves the intelligibility of the second degraded speech passage. We investigate how intelligibility and acoustical structure affects acoustic and linguistic neural representations using multivariate Temporal Response Functions (mTRFs). As expected, behavioral results confirm that perceived speech clarity is improved by priming. TRF analysis reveals that auditory (speech envelope and envelope onset) neural representations are not affected by priming, but only by the acoustics of the stimuli (bottom-up driven). Critically, our findings suggest that segmentation of sounds into words emerges with better speech intelligibility, and most strongly at the later ([~]400 ms latency) word processing stage, in prefrontal cortex (PFC), in line with engagement of top-down mechanisms associated with priming. Taken together, our results show that word representations may provide some objective measures of speech comprehension. Significance StatementElectrophysiological studies have shown that brain tracks different speech features. How these neural tracking measures are modulated by speech intelligibility, however, remained elusive. Using noise-vocoded speech and a priming paradigm, we disentangled the neural effects of intelligibility from the underlying acoustical confounds. Neural intelligibility effects are analyzed at both acoustic and linguistic level using multivariate Temporal Response Functions. Here, we find evidence for an effect of intelligibility and engagement of top-down mechanisms, but only in responses to lexical structure of the stimuli, suggesting that lexical responses are strong candidates for objective measures of intelligibility. Auditory responses are not influenced by intelligibility but only by the underlying acoustic structure of the stimuli.

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bioRxiv2022

Effects of Aging on Cortical Representations of Continuous Speech

Understanding speech in a noisy environment is crucial in day-to-day interactions, and yet becomes more challenging with age, even for healthy aging. Age-related changes in the neural mechanisms that enable speech-in-noise listening have been investigated previously; however, the extent to which age affects the timing and fidelity of encoding of target and interfering speech streams are not well understood. Using magnetoencephalography (MEG), we investigated how continuous speech is represented in auditory cortex in the presence of interfering speech, in younger and older adults. Cortical representations were obtained from neural responses that time-locked to the speech envelopes using speech envelope reconstruction and temporal response functions (TRFs). TRFs showed three prominent peaks corresponding to auditory cortical processing stages: early ([~]50 ms), middle ([~]100 ms) and late ([~]200 ms). Older adults showed exaggerated speech envelope representations compared to younger adults. Temporal analysis revealed both that the age-related exaggeration starts as early as [~]50 ms, and that older adults needed a substantially longer integration time window to achieve their better reconstruction of the speech envelope. As expected, with increased speech masking, envelope reconstruction for the attended talker decreased and all three TRF peaks were delayed, with aging contributing additionally to the reduction. Interestingly, for older adults the late peak was delayed, suggesting that this late peak may receive contributions from multiple sources. Together these results suggest that there are several mechanisms at play compensating for age-related temporal processing deficits at several stages, but which are not able to fully reestablish unimpaired speech perception. NEW & NOTEWORTHYWe observed age-related changes in cortical temporal processing of continuous speech that may be related to older adults difficulty understanding speech in noise. These changes occur in both timing and strength of the speech representations at different cortical processing stages, and depend on both noise condition and selective attention. Critically, their dependency on noise condition changes dramatically among the early, middle, and late cortical processing stages, underscoring how aging differentially affects these stages.

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bioRxiv2022

NLGC: Network Localized Granger Causality with Application to MEG Directional Functional Connectivity Analysis

Identifying the directed connectivity that underlie networked activity between different cortical areas is critical for understanding the neural mechanisms behind sensory processing. Granger causality (GC) is widely used for this purpose in functional magnetic resonance imaging analysis, but there the temporal resolution is low, making it difficult to capture the millisecond-scale interactions underlying sensory processing. Magnetoencephalography (MEG) has millisecond resolution, but only provides low-dimensional sensor-level linear mixtures of neural sources, which makes GC inference challenging. Conventional methods proceed in two stages: First, cortical sources are estimated from MEG using a source localization technique, followed by GC inference among the estimated sources. However, the spatiotemporal biases in estimating sources propagate into the subsequent GC analysis stage, may result in both false alarms and missing true GC links. Here, we introduce the Network Localized Granger Causality (NLGC) inference paradigm, which models the source dynamics as latent sparse multivariate autoregressive processes and estimates their parameters directly from the MEG measurements, integrated with source localization, and employs the resulting parameter estimates to produce a precise statistical characterization of the detected GC links. We offer several theoretical and algorithmic innovations within NLGC and further examine its utility via comprehensive simulations and application to MEG data from an auditory task involving tone processing from both younger and older participants. Our simulation studies reveal that NLGC is markedly robust with respect to model mismatch, network size, and low signal-to-noise ratio, whereas the conventional two-stage methods result in high false alarms and mis-detections. We also demonstrate the advantages of NLGC in revealing the cortical network-level characterization of neural activity during tone processing and resting state by delineating task- and age-related connectivity changes.

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bioRxiv2022

Algorithms for Estimating Time-Locked Neural Response Components in Cortical Processing of Continuous Speech

ObjectiveThe Temporal Response Function (TRF) is a linear model of neural activity time-locked to continuous stimuli, including continuous speech. TRFs based on speech envelopes typically have distinct components that have provided remarkable insights into the cortical processing of speech. However, current methods may lead to less than reliable estimates of single-subject TRF components. Here, we compare two established methods, in TRF component estimation, and also propose novel algorithms that utilize prior knowledge of these components, bypassing the full TRF estimation. MethodsWe compared two established algorithms, ridge and boosting, and two novel algorithms based on Subspace Pursuit (SP) and Expectation Maximization (EM), which directly estimate TRF components given plausible assumptions regarding component characteristics. Single-channel, multi-channel, and source-localized TRFs were fit on simulations and real magnetoencephalographic data. Performance metrics included model fit and component estimation accuracy. ResultsBoosting and ridge have comparable performance in component estimation. The novel algorithms outperformed the others in simulations, but not on real data, possibly due to the plausible assumptions not actually being met. Ridge had slightly better model fits on real data compared to boosting, but also more spurious TRF activity. ConclusionResults indicate that both smooth (ridge) and sparse (boosting) algorithms perform comparably at TRF component estimation. The SP and EM algorithms may be accurate, but rely on assumptions of component characteristics. SignificanceThis systematic comparison establishes the suitability of widely used and novel algorithms for estimating robust TRF components, which is essential for improved subject-specific investigations into the cortical processing of speech.

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bioRxiv2021

Cortical tracking of voice pitch in the presence of multiple speakers depends on selective attention

Voice pitch carries linguistic as well as non-linguistic information. Previous studies have described cortical tracking of voice pitch in clean speech, with responses reflecting both pitch strength and pitch value. However, pitch is also a powerful cue for auditory stream segregation, especially when competing streams have pitch differing in fundamental frequency, as is the case when multiple speakers talk simultaneously. We therefore investigated how cortical speech pitch tracking is affected in the presence of a second, task-irrelevant speaker. We analyzed human magnetoencephalography (MEG) responses to continuous narrative speech, presented either as a single talker in a quiet background, or as a two-talker mixture of a male and a female speaker. In clean speech, voice pitch was associated with a right-dominant response, peaking at a latency of around 100 ms, consistent with previous EEG and ECoG results. The response tracked both the presence of pitch as well as the relative value of the speakers fundamental frequency. In the two-talker mixture, pitch of the attended speaker was tracked bilaterally, regardless of whether or not there was simultaneously present pitch in the speech of the irrelevant speaker. Pitch tracking for the irrelevant speaker was reduced: only the right hemisphere still significantly tracked pitch of the unattended speaker, and only during intervals in which no pitch was present in the attended talkers speech. Taken together, these results suggest that pitch-based segregation of multiple speakers, at least as measured by macroscopic cortical tracking, is not entirely automatic but strongly dependent on selective attention.

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bioRxiv2021

Bilaterally Reduced Rolandic Beta Band Activity in Minor Stroke Patients

Stroke patients with hemiparesis display decreased beta band (13-25 Hz) rolandic activity, correlating to impaired motor function. However, patients without significant weakness, with small lesions far from sensorimotor cortex, nevertheless exhibit bilateral decreased motor dexterity and slowed reaction times. We investigate whether these minor stroke patients also display abnormal beta band activity. Magnetoencephalographic (MEG) data were collected from nine minor stroke patients (NIHSS < 4) without significant hemiparesis, at ~1 and ~6 months postinfarct, and eight age-similar controls. Rolandic relative beta power during matching tasks and resting state, and Beta Event Related (De)Synchronization (ERD/ERS) during button press responses were analyzed. Regardless of lesion location, patients had significantly reduced relative beta power and ERS compared to controls. Abnormalities persisted over visits, and were present in both ipsi- and contra-lesional hemispheres, consistent with bilateral impairments in motor dexterity and speed. Minor stroke patients without severe weakness display reduced rolandic beta band activity in both hemispheres, which may be linked to bilaterally impaired dexterity and processing speed, implicating global connectivity dysfunction affecting sensorimotor cortex. Rolandic beta band activity may be a potential biomarker and treatment target, even for minor stroke patients with small lesions far from sensorimotor areas.

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bioRxiv2021

Eelbrain: A Python toolkit for time-continuous analysis with temporal response functions

1Even though human experience unfolds continuously in time, it is not strictly linear; instead, it entails cascading processes building hierarchical cognitive structures. For instance, during speech perception, humans transform a continuously varying acoustic signal into phonemes, words, and meaning, and these levels all have distinct but interdependent temporal structures. Time-lagged regression using temporal response functions (TRFs) has recently emerged as a promising tool for disentangling electrophysiological brain responses related to such complex models of perception. Here we introduce the Eelbrain Python toolkit, which makes this kind of analysis easy and accessible. We demonstrate its use, using continuous speech as a sample paradigm, with a freely available EEG dataset of audiobook listening. A companion GitHub repository provides the complete source code for the analysis, from raw data to group level statistics. More generally, we advocate a hypothesis-driven approach in which the experimenter specifies a hierarchy of time-continuous representations that are hypothesized to have contributed to brain responses, and uses those as predictor variables for the electrophysiological signal. This is analogous to a multiple regression problem, but with the addition of a time dimension. TRF analysis decomposes the brain signal into distinct responses associated with the different predictor variables by estimating a multivariate TRF (mTRF), quantifying the influence of each predictor on brain responses as a function of time(-lags). This allows asking two questions about the predictor variables: 1) Is there a significant neural representation corresponding to this predictor variable? And if so, 2) what are the temporal characteristics of the neural response associated with it? Thus, different predictor variables can be systematically combined and evaluated to jointly model neural processing at multiple hierarchical levels. We discuss applications of this approach, including the potential for linking algorithmic/representational theories at different cognitive levels to brain responses through computational models with appropriate linking hypotheses.

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bioRxiv2021

Parallel processing in speech perception: Local and global representations of linguistic context

Speech processing is highly incremental. It is widely accepted that human listeners continuously use the linguistic context to anticipate upcoming concepts, words, and phonemes. However, previous evidence supports two seemingly contradictory models of how a predictive context is integrated with the bottom-up sensory input: Classic psycholinguistic paradigms suggest a two-stage process, in which acoustic input initially leads to local, context-independent representations, which are then quickly integrated with contextual constraints. This contrasts with the view that the brain constructs a single coherent, unified interpretation of the input, which fully integrates available information across representational hierarchies, and thus uses contextual constraints to modulate even the earliest sensory representations. To distinguish these hypotheses, we tested magnetoencephalography responses to continuous narrative speech for signatures of local and unified predictive models. Results provide evidence that listeners employ both types of models in parallel. Two local context models uniquely predict some part of early neural responses, one based on sublexical phoneme sequences, and one based on the phonemes in the current word alone; at the same time, even early responses to phonemes also reflect a unified model that incorporates sentence level constraints to predict upcoming phonemes. Neural source localization places the anatomical origins of the different predictive models in non-identical parts of the superior temporal lobes bilaterally, with the right hemisphere showing a relative preference for more local models. These results suggest that speech processing recruits both local and unified predictive models in parallel, reconciling previous disparate findings. Parallel models might make the perceptual system more robust, facilitate processing of unexpected inputs, and serve a function in language acquisition. Impact statementSpeech processing engages multiple predictive models, using sublexical, word- and sentence contexts in parallel to anticipate upcoming phonemes.

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bioRxiv2021

Neural markers of speech comprehension: measuring EEG tracking of linguistic speech representations, controlling the speech acoustics

When listening to speech, our brain responses time-lock to acoustic events in the stimulus. Recent studies have also reported that cortical responses track linguistic representations of speech. However, tracking of these representations is often described without controlling for acoustic properties. Therefore, the response to these linguistic representations might reflect unaccounted acoustic processing rather than language processing. Here, we evaluated the potential of several recently proposed linguistic representations as neural markers of speech comprehension. To do so, we investigated EEG responses to audiobook speech of 29 participants (22 [female]). We examined whether these representations contribute unique information over and beyond acoustic neural tracking and each other. Indeed, not all of these linguistic representations were significantly tracked after controlling for acoustic properties. However, phoneme surprisal, cohort entropy, word surprisal, and word frequency were all significantly tracked over and beyond acoustic properties. We also tested the generality of the associated responses by training on one story and testing on another. In general, the linguistic representations are tracked similarly across different stories spoken by different readers. These results suggests that these representations characterize processing of the linguistic content of speech. Significance StatementFor clinical applications it would be desirable to develop a neural marker of speech comprehension derived from neural responses to continuous speech. Such a measure would allow for behaviour-free evaluation of speech understanding; this would open doors towards better quantification of speech understanding in populations from whom obtaining behavioral measures may be difficult, such as young children or people with cognitive impairments, to allow better targeted interventions and better fitting of hearing devices.

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bioRxiv2021

Cortical Processing of Arithmetic and Simple Sentences in an Auditory Attention Task

Cortical processing of arithmetic and of language rely on both shared and task-specific neural mechanisms, which should also be dissociable from the particular sensory modality used to probe them. Here, spoken arithmetical and non-mathematical statements were employed to investigate neural processing of arithmetic, compared to general language processing, in an attention-modulated cocktail party paradigm. Magnetoencephalography (MEG) data was recorded from 22 subjects listening to audio mixtures of spoken sentences and arithmetic equations while selectively attending to one of the two speech streams. Short sentences and simple equations were presented diotically at fixed and distinct word/symbol and sentence/equation rates. Critically, this allowed neural responses to acoustics, words, and symbols to be dissociated from responses to sentences and equations. Indeed, the simultaneous neural processing of the acoustics of words and symbols was observed in auditory cortex for both streams. Neural responses to sentences and equations, however, was predominantly to the attended stream, and originated primarily from left temporal, and parietal areas, respectively. Additionally, these neural responses were correlated with behavioral performance in a deviant detection task. Source-localized Temporal Response Functions revealed distinct cortical dynamics of responses to sentences in left temporal areas and equations in bilateral temporal, parietal and motor areas. Finally, the target of attention could be decoded from MEG responses, especially in left superior parietal areas. In short, the neural responses to arithmetic and language are especially well segregated during the cocktail party paradigm, and the correlation with behavior suggests that they may be linked to successful comprehension or calculation. Significance StatementNeural processing of arithmetic relies on dedicated, modality independent cortical networks that are distinct from those underlying language processing. Using a simultaneous cocktail party listening paradigm, we found that these separate networks segregate naturally when listeners selectively attend to one type over the other. Time-locked activity in the left temporal lobe was observed for responses to both spoken sentences and equations, but the latter additionally showed bilateral parietal activity consistent with arithmetic processing. Critically, these responses were modulated by selective attention and correlated with task behavior, consistent with reflecting high-level processing for speech comprehension or correct calculations. The response dynamics show task-related differences that were used to reliably decode the attentional target of sentences or equations.

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bioRxiv2019

High Frequency 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. HighlightsO_LICortical MEG responses time-lock at 80-90 Hz to continuous speech C_LIO_LIResponses primarily driven by high gamma rate fluctuations of the speech envelope C_LIO_LIResponse strength and latency are similar for younger and older adults C_LI

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