Biology subjects

Karunathilake, I. M. D.

Publications and source records attributed to Karunathilake, I. M. D..

5 recordsLinked to original sources

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.

neuroscience↗

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

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗