Biology subjects

Gwilliams, L.

Publications and source records attributed to Gwilliams, L..

17 recordsLinked to original sources

bioRxiv2026

Noise Optimization of Basic Signal Component Extraction for Cryogenic and On-Scalp Magnetoencephalography (MEG)

Magnetoencephalography (MEG) measures human neural activity non-invasively with spatio-temporal precision, and has been foundational in enabling impactful discoveries in cognitive neuroscience. New on-scalp MEG sensor technologies, such as OPM-MEG, offer the opportunity to capture more information about the neuronal magnetic fields with higher sensitivity to more complex, higher order spatial components, leading to improved source localization. The accuracy of MEG and OPM-MEG source localization relies on data preprocessing techniques to isolate the neuronal fields from other magnetic and biomagnetic sources through signal space separation, rejection, and suppression methods. Current preprocessing methods risk rejecting brain signals of interest, or can spread sensor noise artifacts unknowingly. Here we propose a novel preprocessing method for MEG, and test the extent to which it overcomes limitations of prior methods. Specifically, we derive, apply, and assess a novel signal space separation (SSS) method with Fosters inverse, a weighted matrix inversion protocol that can utilize information about the MEG sensor noise and artifacts to reconstruct neuronal activity. With simulations, phantom head cryogenic MEG recordings, and subject recordings with two OPM-MEG systems, we show that Fosters inverse with SSS offers a more robust and stable reconstruction of the neuronal magnetic fields, especially in the face of sensor noise and artifacts. As the field of cognitive neuroscience continues to embrace MEG and OPM-MEG, Fosters inverse with SSS offers a robust and powerful data preprocessing technique for reducing noise and improving source localization of the underlying neuronal currents.

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bioRxiv2026

Distinct yet neighboring neural populations encode past, future, and surrounding speech context in the human temporal lobe

Context is critical for both human and artificial speech comprehension systems. While the role of preceding context in speech processing has been well documented, the neural mechanisms supporting the integration of subsequent input -- phonemes and words that occur in the future -- remain poorly understood. Here, we leverage advances in artificial speech systems to model the contribution of different sources of context on the neural encoding of speech in the human brain. For neural encoding, context-informed but not context-uninformed speech model embeddings explain unique variance in human neural activity beyond acoustics, including in early speech processing regions. In particular, model embeddings informed by past, future, and surrounding context explain activity in distinct intracranial electrodes. These electrodes are left-lateralised, and spatially intermixed in the temporal lobe. We find that beyond-word context is crucial for the representational quality of speech model embeddings, and in particular for the encoding of abstract linguistic information. Our finding that spatially neighboring yet distinct neural populations in the temporal lobe encode representations shaped by different contextual sources (past, future, and surrounding input) provides key insight into the neural circuitry that integrates multiple forms of contextual information. Furthermore, our results may inform the downstream use of self-supervised speech representations in language technology tasks, and in models of speech comprehension in the human brain.

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bioRxiv2025

Neural Phoneme Processing in Children with and without Dyslexia

This study investigates the neural dynamics of phoneme processing in 7-year-old children with and without dyslexia (25;9 [male]), using EEG recordings collected during continuous speech listening. By applying temporal generalization to phonetic descriptor decoding, we can disentangle whether potential phoneme processing deficits are due to the maintenance of phonemes in verbal short-term memory and/or inferred differences in phonetic processing speed, both of which are thought to be impaired in dyslexia. We investigated whether phonetic processing depends on the phonemes position or its lexical competition. Our results reveal two key findings that may help explain the challenges faced by children with dyslexia. First, these children exhibit reduced decoding accuracy for word-onset phonemes, suggesting disruptions in either predictive, word-level anticipatory mechanisms or in the intrinsic rhythmic processing aligned with word boundaries. Second, they exhibit increased decoding accuracy for non-onset phonemes with low lexical competition approximately 400 ms after phoneme onset. This pattern suggests that children with dyslexia retain linguistically less relevant sounds longer in verbal short-term memory and process them more slowly compared to their typical reading peers. Together, these findings suggest that dyslexia is characterized by altered phonetic encoding strategies, specifically inefficient prioritization of relevant phonological information. This work provides new insight into the neural mechanisms underlying phonological deficits and contributes to a deeper understanding of the cognitive basis of dyslexia. Significance statementDyslexia is associated with difficulties in phonological processing. Investigating EEG during continuous speech listening, we show that children with dyslexia exhibit weaker encoding of word-onset phonemes and prolonged processing of less informative phonemes. These altered encoding strategies suggest inefficient prioritization of linguistic information, offering new insight into the neural basis of dyslexia.

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bioRxiv2025

Dynamics of auditory word form encoding in human speech cortex

When we hear continuous speech, we perceive it as a series of discrete words, despite the lack of clear boundaries in the acoustic signal. The superior temporal gyrus (STG) encodes phonetic elements like consonants and vowels, but how it extracts whole words as perceptual units remains unclear. Using high-density cortical recordings, we investigated how the brain represents auditory word forms--integrating acoustic-phonetic, prosodic, and lexical features--while participants listened to spoken narratives. Our results show that STG neural populations exhibit a distinctive reset in activity at word boundaries, marked by a brief, sharp drop in cortical activity. Between these resets, the STG consistently encodes distinct acoustic-phonetic, prosodic, and lexical information, supporting the integration of phonological features into coherent word forms. Notably, this process tracks the relative elapsed time within each word, independent of its absolute duration, providing a flexible temporal scaffolding for encoding variable word lengths. We observed similar word form dynamics in the deeper layers of a self-supervised artificial speech network, suggesting a potential convergence with computational models. Additionally, in a bistable word perception task, STG responses were aligned with participants perceived word boundaries on a trial-by-trial basis, further emphasizing the role of dynamic encoding in word recognition. Together, these findings support a new dynamical model of auditory word forms, highlighting their importance as perceptual units for accessing linguistic meaning.

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bioRxiv2024

The spatio-temporal dynamics of phoneme encoding in aging and aphasia

During successful language comprehension, speech sounds (phonemes) are encoded within a series of neural patterns that evolve over time. Here we tested whether these neural dynamics of speech encoding are altered for individuals with a language disorder. We recorded EEG responses from individuals with post-stroke aphasia and healthy age-matched controls (i.e., older adults) during 25 minutes of natural story listening. We estimated the duration of phonetic feature encoding, speed of evolution across neural populations, and the spatial location of encoding over EEG sensors. First, we establish that phonetic features are robustly encoded in EEG responses of healthy older adults. Second, when comparing individuals with aphasia to healthy controls, we find significantly decreased phonetic encoding in the aphasic group after shared initial processing pattern (0.08-0.25s after phoneme onset). Phonetic features were less strongly encoded over left-lateralized electrodes in the aphasia group compared to controls, with no difference in speed of neural pattern evolution. Finally, we observed that healthy controls, but not individuals with aphasia, encode phonetic features longer when uncertainty about word identity is high, indicating that this mechanism - encoding phonetic information until word identity is resolved - is crucial for successful comprehension. Together, our results suggest that aphasia may entail failure to maintain lower-order information long enough to recognize lexical items. Significance statementThis study reveals robust decoding of speech sound properties, so-called phonetic features, from EEG recordings in older adults, as well as decreased phonetic processing in individuals with a language disorder (aphasia) compared to healthy controls. This was most prominent over left-hemispheric electrodes. Additionally, we observed that healthy controls, but not individuals with aphasia, encode phonetic features longer when uncertainty about word identity is high, indicating that this mechanism - encoding phonetic information until word identity is resolved - is crucial for successful language processing. These insights deepen our understanding of disrupted mechanisms in a language disorder, and show how the integration between language processing levels works in the healthy aging, neurotypical brain.

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bioRxiv2024

Dynamics of pitch perception in the auditory cortex

The ability to perceive pitch allows human listeners to experience music, recognize the identity and emotion conveyed by conversational partners, and make sense of their auditory environment. A pitch percept is formed by weighting different acoustic cues (e.g., signal fundamental frequency and inter-harmonic spacing) and contextual cues (expectation). How and when such cues are neurally encoded and integrated remains debated. In this study, twenty-eight participants (16 female) listened to tone sequences with different acoustic cues (pure tones, complex missing fundamental tones, and tones with an ambiguous mixture), placed in predictable and less predictable sequences, while magnetoencephalography was recorded. Decoding analyses revealed that pitch was encoded in neural responses to all three tone types, in the low-to-mid auditory cortex and sensorimotor cortex bilaterally, with right-hemisphere dominance. The pattern of activity generalized across cue-types, offset in time: pitch was neurally encoded earlier for harmonic tones ([~]85ms) than pure tones ([~]95ms). For ambiguous tones, pitch emerged significantly earlier in predictable contexts than unpredictable. The results suggest that a unified neural representation of pitch emerges by integrating independent pitch cues, and that context alters the dynamics of pitch generation when acoustic cues are ambiguous. Significance StatementPitch enables humans to enjoy music, understand the emotional intent of a conversational partner, distinguish lexical items in tonal languages, and make sense of the acoustic environment. The study of pitch has lasted over a century, with conflicting accounts of how and when the brain integrates spectrotemporal information to map different sound sources onto a single and stable pitch percept. Our results answer crucial questions about the emergence of perceptual pitch in the brain: namely, that place and temporal cues to pitch seem to be accounted for by early auditory cortex, that a common representation of perceptual pitch emerges early in the right hemisphere, and that the temporal dynamics of pitch representations are modulated by expectation.

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bioRxiv2024

Hierarchical dynamic coding coordinates speech comprehension in the brain

Speech comprehension involves transforming an acoustic waveform into meaning. To do so, the human brain generates a hierarchy of features that converts the sensory input into increasingly abstract language properties. However, little is known about how rapid incoming sequences of hierarchical features are continuously coordinated. Here, we propose that each language feature is supported by a dynamic neural code, which represents the sequence history of hierarchical features in parallel. To test this Hierarchical Dynamic Coding (HDC) hypothesis, we use time-resolved decoding of brain activity to track the construction, maintenance, and update of a comprehensive hierarchy of language features spanning phonetic, word form, lexical-syntactic, syntactic and semantic representations. For this, we recorded 21 native English participants with magnetoencephalography (MEG), while they listened to two hours of short stories in English. Our analyses reveal three main findings. First, the brain represents and simultaneously maintains a sequence of hierarchical features. Second, the duration of these representations depends on their level in the language hierarchy. Third, each representation is maintained by a dynamic neural code, which evolves at a speed commensurate with its corresponding linguistic level. This HDC preserves the maintenance of information over time while limiting destructive interference between successive features. Overall, HDC reveals how the human brain maintains and updates the continuously unfolding language hierarchy during natural speech comprehension, thereby anchoring linguistic theories to their biological implementations.

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bioRxiv2023

Speech prosody enhances the neural processing of syntax

Human language relies on the correct processing of syntactic information, as it is essential for successful communication between speakers. As an abstract level of language, syntax has often been studied separately from the physical form of the speech signal, thus often masking the interactions that can promote better syntactic processing in the human brain. We analyzed a MEG dataset to investigate how acoustic cues, specifically prosody, interact with syntactic representations. We examined whether prosody enhances the cortical encoding of syntactic representations. We decoded syntactic phrase boundaries directly from brain activity, and evaluated possible modulations of the decoding by the presence of prosodic boundaries. Our findings demonstrate that the presence of a prosodic boundary improves the representation of phrase boundaries, indicating the facilitative role of prosodic cues in processing abstract linguistic features. This study gives neurobiological evidence for the boosting of syntactic processing via interactions with prosody.

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bioRxiv2022

Tracking the online construction of linguistic meaning through negation

Combinatoric linguistic operations underpin human language processes, but how meaning is composed and refined in the mind of the reader is not well understood. We address this puzzle by exploiting the ubiquitous function of negation. We track the online effects of negation ("not") and intensifiers ("really") on the representation of scalar adjectives (e.g., "good") in parametrically designed behavioral and neurophysiological (MEG) experiments. The behavioral data show that participants first interpret negated adjectives as affirmative and later modify their interpretation towards, but never exactly as, the opposite meaning. Decoding analyses of neural activity further reveal significant above chance decoding accuracy for negated adjectives within 600 ms from adjective onset, suggesting that negation does not invert the representation of adjectives (i.e., "not bad" represented as "good"); furthermore, decoding accuracy for negated adjectives is found to be significantly lower than that for affirmative adjectives. Overall, these results suggest that negation mitigates rather than inverts the neural representations of adjectives. This putative suppression mechanism of negation is supported by increased synchronization of beta-band neural activity in sensorimotor areas. The analysis of negation provides a steppingstone to understand how the human brain represents changes of meaning over time.

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bioRxiv2022

Top-down information flow drives lexical access when listening to continuous speech

Speech is often structurally and semantically ambiguous. Here we study how the human brain uses sentence context to resolve lexical ambiguity. Twenty-one participants listened to spoken narratives while magneto-encephalography (MEG) was recorded. Stories were annotated for grammatical word class (noun, verb, adjective) under two hypothesised sources of information: bottom-up: the most common word class given the words phonology; top-down: the correct word class given the context. We trained a classifier on trials where the hypotheses matched (about 90%) and tested the classifier on trials where they mismatched. The classifier predicted top-down word class labels, and anti-correlated with bottom-up labels. Effects peaked [~]100ms after word onset over mid-frontal MEG sensors. Phonetic information was encoded in parallel, though peaking later ([~]200ms). Our results support that during continuous speech processing, lexical representations are quickly built in a context-sensitive manner. We showcase multivariate analyses for teasing apart subtle representational distinctions from neural time series.

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bioRxiv2021

High density single-unit human cortical recordings using the Neuropixels probe

A fundamental unit of neural computation is the action potential. While significant advances have been made in the ability to sample action potentials of large numbers of individual neurons in animal models, translation of these methodologies to humans has been lacking due to clinical time constraints, electrical noise in the operating room, and reliability of the methodology. Here we present a reliable method for intraoperative recording of dozens of neurons in humans using the Neuropixels probe, yielding up to [~]100 simultaneously-recorded single-units (n=596 across 11 recordings in 8 participants). Most single-units were active within 1 minute of reaching target depth, compatible with clinical time constraints. Cell pairs active close in time were spatially closer in most recordings, demonstrating the power to resolve complex cortical dynamics. Altogether, this approach provides access to population single-unit activity across the depth of human neocortex at scales previously only accessible in animal models. HighlightsO_LISingle units in 8 patients, yielding 596 putative single units across 11 recordings C_LIO_LIThe majority of putative neurons fire at least 1 spike within one minute after reaching target depth C_LIO_LIPutative neurons take longer to fire at least one spike in anesthetized compared to awake participants C_LIO_LICell-pairs that fire action potentials close in time are spatially closer together than those fire further apart in time C_LI

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bioRxiv2021

Spontaneous neural oscillations influence behavior and sensory representations by suppressing neuronal excitability

The ability to process and respond to external input is critical for adaptive behavior. Why, then, do neural and behavioral responses vary across repeated presentations of the same sensory input? Spontaneous fluctuations of neuronal excitability are currently hypothesized to underlie the trial-by-trial variability in sensory processing. To test this, we capitalized on invasive electrophysiology in neurosurgical patients performing an auditory discrimination task with visual cues: specifically, we examined the interaction between prestimulus alpha oscillations, excitability, task performance, and decoded neural stimulus representations. We found that strong prestimulus oscillations in the alpha+ band (i.e., alpha and neighboring frequencies), rather than the aperiodic signal, correlated with a low excitability state, indexed by reduced broadband high-frequency activity. This state was related to slower reaction times and reduced neural stimulus encoding strength. We propose that the alpha+ rhythm modulates excitability, thereby resulting in variability in behavior and sensory representations despite identical input.

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bioRxiv2020

Neural dynamics of phoneme sequencing in real speech jointly encode order and invariant content

Speech consists of a continuously-varying acoustic signal. Yet human listeners experience it as a sequence of discrete units (phonemes and words). To identify how the brain continuously represents and updates speech units, we recorded two-hour magnetoencephalograms from 21 subjects listening to short narratives. Our encoding and decoding analyses show that the human brain uses a dynamic coding scheme to represent approximately three successive phonetic features at each time sample. These dynamic phonetic representations, not directly accessible from the acoustic signal, are active earlier when phonemes are more predictable, and are sustained when lexical identity is uncertain. The results reveal how phoneme sequences in natural speech are neurally represented and shed new light on how the human brain combines them to probe the mental lexicon. One sentence summaryThe human brain keeps track of the relative order of speech sound sequences by jointly encoding content and elapsed processing time

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bioRxiv2019

Neural adaptation to accented speech: prefrontal cortex aids attunement in auditory cortices

Speech is a complex and ambiguous acoustic signal that varies significantly within and across speakers. A prevalent and ubiquitous example of such variation is accented speech, to which humans adapt extremely rapidly. The goal of this study is to uncover the neurobiological bases of the attunement process that enables such fluent comprehension. Twenty-four native English participants listened to words spoken by an unaccented "canonical" American talker and two "accented" talkers, and performed a word-picture matching task, while magnetoencephalography (MEG) was recorded. Accented speech was created by including systematic phonological substitutions within the word (e.g. [s] [->] [sh]). Activity in the auditory cortex (superior temporal gyrus) was greater for accented speech, but, critically, this was not attenuated by exposure. By contrast, prefrontal regions showed an interaction between the presence of an accent and amount of exposure: while activity decreased for canonical speech over time, responses to accented speech remained consistently elevated. Grainger causality analyses further revealed that prefrontal responses serve to modulate activity in auditory regions, suggesting the recruitment of top-down processing to decode accented signal. In sum, our results show that accented speech does not elicit the same prefrontal reduction in amplitude over time that unaccented speech does, and points to a dynamic exchange of information between the prefrontal and auditory cortices in order to recalculate phonetic classification and subsequent identification of lexical items. Significance statementHuman ability to adapt to different peoples idiosyncratic pronunciations is a hallmark of speech comprehension. This study aims to address whether adaptation to speakers accents manifests itself at the perceptual level (i.e. through adaptation of low-level neural responses in the auditory cortex) or at the post-perceptual level (i.e. higher-order regions correcting the sensory signal received from auditory cortex). Our results support the post-perceptual hypothesis: we found that responses in auditory cortex emitted an error-like signal that was invariant to exposure; whereas responses in the prefrontal cortex were modulated by exposure. These findings provide initial insight into accent adaptation, and illuminate the computational stages supporting speech comprehension more generally.

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bioRxiv2019

Recurrent Processes Emulate a Cascade of Hierarchical Decisions: Evidence from Spatio-Temporal Decoding of Human Brain Activity

Mounting evidence suggests that perception depends on a largely-feedforward brain network. However, the discrepancy between (i) the latency of the corresponding feedforward responses (150-200 ms) and (ii) the time it takes human subjects to recognize brief images (often >500 ms) suggests that recurrent neuronal activity is critical to visual processing. Here, we use magneto-encephalography to localize, track and decode the feedforward and recurrent responses elicited by brief presentations of variably-ambiguous letters and digits. We first confirm that these stimuli trigger, within the first 200 ms, a feedforward response in the ventral and dorsal cortical pathways. The subsequent activity is distributed across temporal, parietal and prefrontal cortices and leads to a slow and incremental cascade of representations culminating in action-specific motor signals. We introduce an analytical framework to show that these brain responses are best accounted for by a hierarchy of recurrent neural assemblies. An accumulation of computational delays across specific processing stages explains subjects reaction times. Finally, the slow convergence of neural representations towards perceptual categories is quickly followed by all-or-none motor decision signals. Together, these results show how recurrent processes generate, over extended time periods, a cascade of hierarchical decisions that ultimately predicts subjects perceptual reports.

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bioRxiv2017

Performance-optimized hierarchical models only partially predict neural responses during perceptual decision making

Models of perceptual decision making have historically been designed to maximally explain behaviour and brain activity independently of their ability to actually perform tasks. More recently, performance-optimized models have been shown to correlate with brain responses to images and thus present a complementary approach to understand perceptual processes. In the present study, we compare how these approaches comparatively account for the spatio-temporal organization of neural responses elicited by ambiguous visual stimuli. Forty-six healthy human subjects performed perceptual decisions on briefly flashed stimuli constructed from ambiguous characters. The stimuli were designed to have 7 orthogonal properties, ranging from low-sensory levels (e.g. spatial location of the stimulus) to conceptual (whether stimulus is a letter or a digit) and task levels (i.e. required hand movement). Magneto-encephalography source and decoding analyses revealed that these 7 levels of representations are sequentially encoded by the cortical hierarchy, and actively maintained until the subject responds. This hierarchy appeared poorly correlated to normative, drift-diffusion, and 5-layer convolutional neural networks (CNN) optimized to accurately categorize alpha-numeric characters, but partially matched the sequence of activations of 3/6 state-of-the-art CNNs trained for natural image labeling (VGG-16, VGG-19, MobileNet). Additionally, we identify several systematic discrepancies between these CNNs and brain activity, revealing the importance of single-trial learning and recurrent processing. Overall, our results strengthen the notion that performance-optimized algorithms can converge towards the computational solution implemented by the human visual system, and open possible avenues to improve artificial perceptual decision making.

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bioRxiv2017

In spoken word recognition the future predicts the past

Speech is an inherently noisy and ambiguous signal. In order to fluently derive meaning, a listener must integrate contextual information to guide interpretations of the sensory input. While many studies have demonstrated the influence of prior context, the neural mechanisms supporting the integration of subsequent information remain unknown. Using magnetoencephalography, we analysed responses to spoken words with a varyingly ambiguous onset phoneme, the identity of which is later disambiguated at the lexical uniqueness point1. Our results uncover a three-level processing network. Subphonemic detail is preserved in primary auditory cortex over long timescales, and re-evoked at subsequent phoneme positions. Commitments to phonological categories occur in parallel, resolving on the shorter time-scale of ~450 ms. Finally, predictions are formed over likely lexical items. These findings provide evidence that future input determines the perception of earlier speech sounds by maintaining sensory features until they can be optimally integrated with top-down information.

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