Biology subjects

Doyle, W. K.

Publications and source records attributed to Doyle, W. K..

9 recordsLinked to original sources

bioRxiv2026

Prediction of Stimulation-Defined Eloquent Cortex Using Graph-Theoretical Connectivity from Electrocorticography During Presurgical Mapping

Background and ObjectivesElectrical stimulation mapping (ESM) is the clinical gold standard for identifying eloquent cortex during presurgical evaluation but is time-intensive, constrained by incomplete cortical sampling, and limited by patient tolerance. We investigated whether features derived from electrocorticography (ECoG), including functional connectivity measures, can provide complementary information for identifying functionally critical cortex and examined how predictive performance varies across functional domains, behavioral tasks, and data quantity. MethodsFourteen patients undergoing intracranial monitoring for epilepsy surgery performed speech production tasks while ECoG was recorded independently of stimulation mapping. Graph-theoretic functional connectivity features derived from high-gamma activity (70-150 Hz), combined with anatomical region encoding, were used to train machine learning classifiers, and predictive performance was evaluated using leave-one-subject-out validation with ESM-defined functional deficits used as ground-truth outcomes. ResultsFourteen patients were included. Performance differed across functional domains, with motor-critical electrodes identified with the highest accuracy (ROC-AUC 0.929 {+/-} 0.061; PR-AUC 0.755 {+/-} 0.191), followed by speech arrest (ROC-AUC 0.793 {+/-} 0.103; PR-AUC 0.550 {+/-} 0.196), whereas language-critical electrodes were more difficult to robustly predict (ROC-AUC 0.761 {+/-} 0.160; PR-AUC 0.385 {+/-} 0.167). Additional signal-derived features provided limited benefit beyond anatomical and connectivity features. Performance also varied across tasks and with the number of available trials, with motor prediction remaining stable across trial counts and speech arrest prediction improving with increasing trial counts up to approximately 10 trials before plateauing. In addition, evaluation at the level of stimulation pairs increased sensitivity, with only modest changes in overall performance. DiscussionGraph-theoretic connectivity analysis of ECoG provides complementary information for identifying stimulation-defined functional criticality and supports presurgical mapping. Differences in predictive performance across functional domains likely reflect underlying neuro-physiologic organization, with connectivity providing the clearest improvement for speech arrest prediction. Combined-label prediction strategies increased sensitivity at the expense of specificity, reflecting a clinically relevant tradeoff between broad detection of critical cortex and precise localization. These findings suggest that connectivity-informed approaches may help guide task selection and improve mapping efficiency while complementing electrical stimulation mapping.

neuroscience↗

bioRxiv2024

Information-making processes in the speaker's brain drive human conversations forward

Natural human conversation is driven by the exchange of information-rich messages that surprise the listener and deviate from predictable context. While extensive research has characterized how the brain processes unexpected linguistic input during comprehension, the neural mechanisms underlying the generation of such information by the speaker remain poorly understood. In this study, we hypothesize that the speakers brain actively creates information through extra neural computation, transforming internal thoughts into novel, meaningful linguistic outputs. Utilizing a unique 24/7 dataset of continuous electrocorticography (ECoG) comprising approximately 100 hours of spontaneous natural conversations, we contrast the neural basis of speech production and comprehension within the same participants. Behaviorally, we find that speakers take longer pausing for an additional 100-150 milliseconds before producing information-rich, improbable words, even when controlling for word identity and frequency. Neurally, we provide converging evidence for a previously unreported process: generating information-rich words elicits significantly stronger neural activity (ERP) and enhanced neural encoding and decoding in language-related areas starting 100-500 ms before articulation. This pattern contrasts sharply with speech comprehension, where enhanced neural activity for predictable words occurs before onset, while responses to improbable words emerge only after onset as prediction errors. Furthermore, we demonstrate that large language models (LLMs) mirror this biological process, requiring deeper internal computation across layers to generate improbable versus probable words. Together, these results reveal that the speakers brain is not merely a transmission channel but an active generator of information, recruiting extra neural resources to create novel content that diverges from listener expectations.

neuroscience↗

bioRxiv2023

Do cortical co-ripples bind lines, letters, words, meanings, strategy and action in reading?

Whether high-frequency phase-locked oscillations facilitate integration ( binding) of information across widespread cortical areas is controversial. Here we show with intracranial EEG that cortico-cortical co-ripples ([~]100ms long [~]90Hz oscillations) increase during reading and semantic decisions, at the times and co-locations when and where binding should occur. Fusiform wordform areas co-ripple with virtually all language areas, maximally from 200-400ms post-word-onset. Semantically-specified target words evoke strong co-rippling between wordform, semantic, executive and response areas from 400-800ms, with increased co-rippling between semantic, executive and response areas prior to correct responses. Co-ripples were phase-locked at zero-lag over long distances (>12cm), especially when many areas were co-rippling. General co-activation, indexed by non-oscillatory high gamma, was mainly confined to early latencies in fusiform and earlier visual areas, preceding co-ripples. These findings suggest that widespread synchronous co-ripples may assist the integration of multiple cortical areas for sustained periods during cognition. One Sentence SummaryWidespread visual, wordform, semantic, executive and response areas phase-lock at 90Hz during integrative semantic processing.

neuroscience↗

bioRxiv2021

The timescale and magnitude of aperiodic activity decreases with cortical depth in humans, macaques and mice

Cortical dynamics obey a 1/f power law, exhibiting an exponential decay of spectral power with increasing frequency. The slope and offset of this 1/f decay reflect the timescale and magnitude of aperiodic neural activity, respectively. These properties are tightly linked to cellular and circuit mechanisms (e.g. excitation:inhibition balance and firing rates) as well as cognitive processes (e.g. perception, memory, and state). However, the physiology underlying the 1/f power law in cortical dynamics is not well understood. Here, we compared laminar recordings from human, macaque and mouse cortex to evaluate how 1/f aperiodic dynamics vary across cortical layers and species. We report that 1/f slope is steepest in superficial layers and flattest in deep layers in each species. Additionally, the magnitude of this 1/f decay is greatest in superficial cortex and decreases with depth. We could account for both of these findings with a simple model in which superficial cortical transmembrane currents had longer time constants and greater densities than those in deeper layers. Together, our results provide novel insight into the organization of cortical dynamics, suggesting that the amplitude and time constant of local currents control circuit processing as a function of laminar depth. This may represent a general mechanism to facilitate appropriate integration of fast sensory inputs (infragranular) with slow feedback-type inputs (supragranular) across cortical areas and species.

neuroscience↗

bioRxiv2021

Imagined speech can be decoded from low- and cross-frequency features in perceptual space

Reconstructing intended speech from neural activity using brain-computer interfaces (BCIs) holds great promises for people with severe speech production deficits. While decoding overt speech has progressed, decoding imagined speech have met limited success, mainly because the associated neural signals are weak and variable hence difficult to decode by learning algorithms. Using three electrocorticography datasets totalizing 1444 electrodes from 13 patients who performed overt and imagined speech production tasks, and based on recent theories of speech neural processing, we extracted consistent and specific neural features usable for future BCIs, and assessed their performance to discriminate speech items in articulatory, phonetic, vocalic, and semantic representation spaces. While high-frequency activity provided the best signal for overt speech, both low- and higher-frequency power and local cross-frequency contributed to successful imagined speech decoding, in particular in phonetic and vocalic, i.e. perceptual, spaces. These findings demonstrate that low-frequency power and cross-frequency dynamics contain key information for imagined speech decoding, and that exploring perceptual spaces offers a promising avenue for future imagined speech BCIs.

neuroscience↗

bioRxiv2020

Moment-by-moment tracking of naturalistic learning and its underlying hippocampo-cortical interactions

Every day our memory system achieves a remarkable feat: We form lasting memories of stimuli that were only encountered once. Here we investigate such learning as it naturally occurs during story listening, with the goal of uncovering when and how memories are stored and retrieved during processing of continuous, naturalistic stimuli. In behavioral experiments we confirm that, after a single exposure to a naturalistic story, participants can learn about its structure and are able to recall upcoming words in the story. In patients undergoing electrocorticographic recordings, we then track mnemonic information in high frequency activity (70 - 200Hz) as patients listen to a story twice. In auditory processing regions we demonstrate the rapid reinstatement of upcoming information after a single exposure; this neural measure of predictive recall correlates with behavioral measures of event segmentation and learning. Connectivity analyses on the neural data reveal information-flow from cortex to hippocampus at the end of events. On the second time of listening information-flow from hippocampus to cortex precedes moments of successful reinstatement.

neuroscience↗

bioRxiv2017

Superficial Slow Rhythms Integrate Cortical Processing in Humans

The neocortex is composed of six anatomically and physiologically specialized layers. It has been proposed that integration of activity across cortical areas is mediated anatomically by associative connections terminating in superficial layers, and physiologically by slow cortical rhythms. However, the means through which neocortical anatomy and physiology interact to coordinate neural activity remains obscure. Using laminar microelectrode arrays in 19 human participants, we found that most EEG activity is below 10-Hz (delta/theta) and generated by superficial cortical layers during both wakefulness and sleep. Cortical surface grid, grid-laminar, and dual-laminar recordings demonstrate that these slow rhythms are synchronous within upper layers across broad cortical areas. The phase of this superficial slow activity is reset by infrequent stimuli and coupled to the amplitude of faster oscillations and neuronal firing across all layers. These findings support a primary role of superficial slow rhythms in generating the EEG and integrating cortical activity.

neuroscience↗

bioRxiv2017

The Generation and Propagation of the Human Alpha Rhythm

IntroductionThe alpha rhythm (7-13 Hz) is the longest studied brain oscillation and has been theorized to play a key role in cognition. Still, substantial uncertainty remains over its physiology. In this study, we used micro and macro electrodes in patients undergoing surgery for epilepsy to measure the intracortical and thalamic generators of the human alpha rhythm. We first found that alpha propagates from higher-order anterosuperior cortex towards the lower-order occipital poles, consistent with alpha effecting top-down processing. This cortical alpha drives thalamic alpha, reversing prevailing theories of a thalamic alpha pacemaker. Finally, alpha is dominated by currents and firing in supragranular cortex, contravening the popular conception of alpha as an infragranular rhythm. Together, these results demonstrate that the alpha rhythm reflects short-range supragranular feedback which propagates from higher-order to lower order cortex and cortex to thalamus. These physiological insights explain how alpha could mediate feedback throughout the thalamocortical system.

neuroscience↗

bioRxiv2017

Time-resolved neural reinstatement and separation during memory decisions in human hippocampus

Mnemonic decision-making has long been hypothesized to rely on hippocampal dynamics that bias memory processing toward the formation of new memories or the retrieval of old ones. Successful memory encoding would be best optimized by pattern separation, whereby two highly similar experiences can be represented by underlying neural populations in an orthogonal manner. By contrast, successful memory retrieval is thought to be supported by a recovery of the same neural pattern laid down during encoding. Here we examined how hippocampal pattern completion and separation emerge over time during memory decisions. We measured electrocorticography activity in the human hippocampus and posterior occipitotemporal cortex (OTC) while participants performed continuous recognition of items that were new, repeated (old), or highly similar to a prior item (similar).During retrieval decisions of old items, both regions exhibited significant reinstatement of multivariate high frequency activity (HFA) associated with encoding. Further, the extent of reinstatement of encoding patterns during retrieval was correlated both with the strength (HFA power) of hippocampal encoding and with the strength of hippocampal retrieval. Evidence for encoding pattern reinstatement was also seen in OTC on trials requiring fine-grained discrimination of similar items. By contrast, hippocampal activity showed evidence for pattern separation during these trials. Together, these results underscore the critical role of the hippocampus in supporting both reinstatement of overlapping information and separation of similar events.\n\nSignificance StatementOne of the biggest computational challenges the memory systems faces is to disambiguate highly similar experiences while at the same time preserving and reinstating prior memories. Remarkably, hippocampal processes have been implicated in both of these functions. However, how this is accomplished is unknown. Leveraging the spatiotemporal resolution of electrocorticography, we found evidence for memory reinstatement in both the hippocampus and occipitotemporal cortex. Reinstatement was differentiated in time across these two regions with earlier reinstatement evident in occipitotemporal cortex. Interestingly, when a current experience was very similar, but not identical to a prior one, occipitotemporal cortical activity still showed reinstatement of the prior memory but hippocampal activity differentiated or disambiguated these two similar experiences.

neuroscience↗