Biology subjects

Wallace, M. T.

Publications and source records attributed to Wallace, M. T..

10 recordsLinked to original sources

bioRxiv2026

Dissociable Stimulus- and Response-Driven Serial Dependence Effects in Auditory, Visual, and Audiovisual Motion Judgments

Efficient motion perception requires integrating immediate sensory evidence with internal representations shaped by experience. This influence of recent history can produce serial dependence effects (SDEs), in which current judgments are biased toward or away from the source of previous information. We examined SDEs in auditory, visual, and cued audiovisual motion-direction discrimination using aggregate psychometric measures and trial-level generalized linear mixed-effects models (GLMMs), which estimated the independent contributions of the previous stimulus direction and the participant's previous response. Participants completed unisensory (n=57) and cued audiovisual (n=48) motion-discrimination tasks. Aggregate analyses indicated attractive SDEs in auditory conditions, repulsive SDEs in visual conditions, and no net SDE in the audiovisual-cued condition. Trial-level modeling revealed a more nuanced structure: previous stimulus direction produced repulsive effects in both unisensory conditions, with previous visual direction influencing judgments across all conditions containing visual motion, whereas previous response produced attractive effects only when visual information was task-relevant. In the audiovisual-cued condition, the absence of a net psychometric shift was consistent with the opposing influences of an attractive response-history effect and a repulsive visual stimulus-history effect rather than an absence of serial dependence. These findings suggest that serial dependence in motion perception reflects dissociable and concurrently operating processes, including a predominantly visual, adaptation-like stimulus-history effect and a task-dependent, decision-related response-history effect.

neuroscience↗

bioRxiv2025

Behavioral evidence for visually dominant audiovisual motion integration

AO_SCPLOWBSTRACTC_SCPLOWAccurate motion perception is crucial for navigating complex environments, where sensory information is often derived from multiple modalities. While multisensory cues improve performance in tasks involving stationary stimuli, the integration of dynamic motion cues is less understood. Here, we examined the nature of auditory and visual motion integration and determined whether attentional instructions modulate integration. Sixty participants performed direction discrimination tasks under auditory, visual, and audiovisual conditions, with cueing instructions to attend to one or both modalities. Results showed that visual motion direction estimates were more reliable than auditory ones, and audiovisual estimates were superior to either modality alone. Additionally, visual motion estimates were more stable that auditory motion estimates during direction conflict trials. Reliability-weighted models of integration closely predicted audiovisual performance, demonstrating optimal integration. Sensory dominance (visual vs. auditory) influenced sensitivity to audiovisual motion, with visual dominant participants performing better overall. Our results demonstrate that the perception of audiovisual motion is dominated by vision and is shaped by top-down attention.

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bioRxiv2024

Brain-state modeling using electroencephalography: Application to adaptive closed-loop neuromodulation for epilepsy

The progress of developing an effective closed-loop neuromodulation system for many neurological pathologies is hindered by the difficulties in accurately capturing a useful representation of a brains instantaneous functional state. Existing approaches rely on expert labeling of electroencephalography data to develop biomarkers of neurophysiological pathology. These techniques do not capture the highly complex functional states of the brain that are presumed to exist between labeled states or allow for the likely possibility of variation among identically labeled states. Thus, we propose BrainState, a self-supervised technique to model an arbitrarily complex instantaneous functional state of a brain using neural multivariate timeseries data. Application of BrainState to intracranial electroencephalography data from patients with epilepsy was able to capture diverse pre-seizure states and quantify nuanced effects of neuromodulation. We anticipate that BrainState will enable the development of sophisticated closed-loop neuromodulation systems for a diverse array of neurological pathologies.

neuroscience↗

bioRxiv2022

The Interictal Suppression Hypothesis in Focal Epilepsy: Electrographic and Structural Evaluation

Why are people with focal epilepsy not constantly seizing? Previous molecular work has implicated gamma-aminobutyric acid balance as integral to seizure generation and termination, but is the high-level distributed brain network involved in suppressing seizures? Recent intracranial electrographic evidence has suggested that seizure onset zones have an increased inward connectivity. Accordingly, we hypothesize that seizure onset zones are actively suppressed by the rest of the brain network during interictal states. We tested this hypothesis on 81 subjects with drug resistant focal epilepsy undergoing presurgical evaluation. We utilized intracranial electrographic resting-state and neurostimulation recordings to evaluate the network connectivity of seizure onset, propagative, and non-involved regions. We then utilized diffusion imaging to acquire estimates of white matter connectivity to evaluate structure-function coupling effects on connectivity findings. Finally, using our observations, we generated a resting-state classification model to assist clinicians in detecting seizure onset and propagative zones without the need for multiple ictal recordings. Our findings indicate that seizure onset and propagative zones demonstrate markedly increased inward connectivity and decreased outward connectivity on both resting-state and neurostimulation analyses. When controlling for distance between regions, the difference between inward vs. outward connectivity remained stable up to 80 mm between brain connections. Structure-function coupling analyses revealed that seizure onset zones exhibit abnormally enhanced coupling (hypercoupling) of surrounding regions compared to presumably healthy tissue. Using these observations, our classification models achieved a maximum held-out testing set accuracy of 92.0{+/-}2.2%. These results indicate that seizure onset zones are actively segregated and suppressed by a widespread brain network. Furthermore, this electrographically observed functional suppression is disproportionate to any observed structural connectivity alterations of the seizure onset zones. These findings have implications for the identification of seizure onset zones using only brief resting-sate recordings to reduce patient morbidity and augment the presurgical evaluation of drug resistant epilepsy. Furthermore, testing of the interictal suppression hypothesis can provide insight into potential new resective, ablative, and neuromodulation approaches to improve surgical success rates in those suffering from drug resistant focal epilepsy.

neuroscience↗

bioRxiv2022

Localizing temporal lobe seizure onset zones using deep learning on SEEG cortico-cortical evoked potentials

In drug resistant temporal lobe epilepsy, automated tools for seizure onset zone (SOZ) localization using brief interictal recordings would supplement presurgical evaluations and improve care. Thus, we sought to localize SOZs by training a multi-channel convolutional neural network on stereo-EEG (SEEG) cortico-cortical evoked potentials. We performed single pulse electrical stimulation with 10 drug resistant temporal lobe epilepsy patients implanted with SEEG. Using the 500,000 unique post-stimulation SEEG epochs, we trained a multi-channel one-dimensional convolutional neural network to determine whether an SOZ was stimulated. SOZs were classified with a mean leave-one-patient-out testing sensitivity of 78.1% and specificity of 74.6%. To achieve maximum accuracy, the model requires a 0-350 ms post stimulation time period. Post-hoc analysis revealed that the model accurately classified unilateral vs bilateral mesial temporal lobe seizure onset, and neocortical SOZs. This is the first demonstration, to our knowledge, that a deep learning framework can be used to accurately classify SOZs using cortico-cortical evoked potentials. Our findings suggest accurate classification of SOZs relies on a complex temporal evolution of evoked potentials within 350 ms of stimulation. Validation in a larger dataset could provide a practical clinical tool for the presurgical evaluation of drug resistant epilepsy.

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bioRxiv2021

Getting the gist faster: Blurry images enhance the early temporal similarity between neural signals and convolutional neural networks

Humans are able to recognize objects under a variety of noisy conditions, so models of the human visual system must account for how this feat is accomplished. In this study, we investigated how image perturbations, specifically reducing images to their low spatial frequency (LSF) components, affected correspondence between convolutional neural networks (CNNs) and brain signals recorded using magnetoencephalography (MEG). Using the high temporal resolution of MEG, we found that CNN-Brain correspondence for deeper and more complex layers across CNN architectures emerged earlier for LSF images than for their unfiltered broadband counterparts. The early emergence of LSF components is consistent with the coarse-to-fine theoretical framework for visual image processing, but surprisingly shows that LSF signals from images are more prominent when high spatial frequencies are removed. In addition, we decomposed MEG signals into oscillatory components and found correspondence varied based on frequency bands, painting a full picture of how CNN-Brain correspondence varies with time, frequency, and MEG sensor locations. Finally, we varied image properties of CNN training sets, and found marked changes in CNN processing dynamics and correspondence to brain activity. In sum, we show that image perturbations affect CNN-Brain correspondence in unexpected ways, as well as provide a rich methodological framework for assessing CNN-Brain correspondence across space, time, and frequency.

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bioRxiv2020

The neural computations for stimulus presence and modal identity diverge along a shared circuit

The brain is comprised of neural circuits that are able to flexibly represent the complexity of the external world. In accomplishing this feat, one of the first attributes the brain must code for is whether a stimulus is present and subsequently what sensory information that stimulus contains. One of the core characteristics of that information is which sensory modality(ies) are being represented. How information regarding both the presence and modal identity of a given stimulus is represented and transformed within the brain remains poorly understood. In this study, we investigated how the brain represents the presence and modal identity of a given stimulus while tactile, audio, and audio-tactile stimuli were passively presented to non-human primates. We recorded spiking activity from primary somatosensory (S1) and ventral pre-motor (PMv) cortices, two areas known to be instrumental in transforming sensory information into motor commands for action. Using multivariate analyses to decode stimulus presence and identity, we found that information regarding stimulus presence and modal identity were found in both S1 and PMv and extended beyond the duration of significant evoked spiking activity, and that this information followed different time-courses in these two areas. Further, we combined time-generalization decoding with cross-area decoding to demonstrate that while signaling the presence of a stimulus involves a feedforward-feedback coupling between S1-PMv, the processing of modal identity is largely restricted to S1. Together, these results highlight the differing spatiotemporal dynamics of information flow regarding stimulus presence and modal identity in two nodes of an important cortical sensorimotor circuit. Significance StatementIt is unclear how the structure and function of the brain support differing sensory functions, such as detecting the presence of a stimulus in the environment vs. identifying it. Here, we used multivariate decoding methods on monkey neuronal data to track how information regarding stimulus presence and modal identity flow within a sensorimotor circuit. Results demonstrate that while neural patterns in both primary somatosensory (S1) and ventral pre-motor (PMv) cortices can be used to detect and discriminate between stimuli, they follow different time-courses. Importantly, findings suggest that while information regarding the presence of a stimulus flows reciprocally between S1 and PMv, information regarding stimulus identity is largely contained in S1.

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bioRxiv2019

Stimulus-specific information flow across the canonical cortical microcircuit

The vast majority of mammalian neocortex consists of a stereotypical microcircuit, the canonical cortical microcircuit (CCM), consisting of a granular input layer, positioned between superficial and deep layers. Due to this uniform layout, neuronal activation tends to follow a similar laminar sequence, with unique information extracted at each step. For example, the primate primary visual cortex (V1) combines the two eyes signals, extracts stimulus orientation and modulates its activity depending on stimulus history. Several theories have been proposed on when and where these processes happen within the CCMs laminar activation sequence, but it has been methodologically challenging to test these hypotheses. Here, we use time-resolved multivariate pattern analysis (MVPA) to decode information regarding the eye-of-origin, stimulus orientation and stimulus repetition from simultaneously measured spiking responses across V1s laminar microcircuit. We find that eye-of-origin information was decodable for the entire duration of stimulus presentation, but diminished in the deepest layers of V1, consistent with the notion that two eyes signals are combined within the upper layers. Conversely, orientation information was transient and equally pronounced across the microcircuit, in line with the idea that this information is relayed to other areas for further processing. Moreover, when stimuli were repeated, information regarding orientation was enhanced at the expense of eye-of origin information, suggesting that V1 modulates information flow to optimize specific stimulus dimensions. Taken together, these findings provide empirical evidence that adjudicates between long-standing hypotheses and reveals how information transfer within the CCM supports unique cortical functions.\n\nSignificance StatementDespite the brains daunting complexity, there are common organizing principles across brain areas. For example, neocortical activation follows a stereotypical pattern that spreads from input layers towards layers above and below. While this activation pattern is well known, it has been challenging to ascertain how unique types of information are extracted within this common sequence in different brain areas. Here we use machine learning to track the flow of stimulus-specific information across the layers of visual cortex. We found that information regarding several separate stimulus dimensions was routed uniquely within the common activation sequence in a manner that confirmed prior model predictions. This finding demonstrates how differences in information flow within the stereotypical neocortical activation sequence shape area-specific functions.

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bioRxiv2019

Selective enhancement of object representations through multisensory integration

Objects are the fundamental building blocks of how we create a representation of the external world. One major distinction amongst objects is between those that are animate versus inanimate. Many objects are specified by more than a single sense, yet the nature by which multisensory objects are represented by the brain remains poorly understood. Using representational similarity analysis of human EEG signals, we show enhanced encoding of audiovisual objects when compared to their corresponding visual and auditory objects. Surprisingly, we discovered the often-found processing advantages for animate objects was not evident in a multisensory context due to greater neural enhancement of inanimate objects--the more weakly encoded objects under unisensory conditions. Further analysis showed that the selective enhancement of inanimate audiovisual objects corresponded with an increase in shared representations across brain areas, suggesting that neural enhancement was mediated by multisensory integration. Moreover, a distance-to-bound analysis provided critical links between neural findings and behavior. Improvements in neural decoding at the individual exemplar level for audiovisual inanimate objects predicted reaction time differences between multisensory and unisensory presentations during a go/no-go animate categorization task. Interestingly, links between neural activity and behavioral measures were most prominent 100 to 200ms and 350 to 500ms after stimulus presentation, corresponding to time periods associated with sensory evidence accumulation and decision-making, respectively. Collectively, these findings provide key insights into a fundamental process the brain uses to maximize information it captures across sensory systems to perform object recognition.\n\nSignificance StatementOur world is filled with an ever-changing milieu of sensory information that we are able to seamlessly transform into meaningful perceptual experience. We accomplish this feat by combining different features from our senses to construct objects. However, despite the fact that our senses do not work in isolation but rather in concert with each other, little is known about how the brain combines the senses together to form object representations. Here, we used EEG and machine learning to study how the brain processes auditory, visual, and audiovisual objects. Surprisingly, we found that non-living objects, the objects which were more difficult to process with one sense alone, benefited the most from engaging multiple senses.

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bioRxiv2018

Multisensory perception reflects individual differences in processing temporal correlations

Sensory signals originating from a single event, such as audiovisual speech, are temporally correlated. Correlated signals are known to facilitate multisensory integration and binding. We sought to further elucidate the nature of this relationship, hypothesizing that multisensory perception will vary with the strength of audiovisual correlation. Human participants detected near-threshold amplitude modulations in auditory and/or visual stimuli. During audiovisual trials, the frequency and phase of auditory modulations were varied, producing signals with a range of correlations. After accounting for individual differences which likely reflect relative temporal processing abilities of participants auditory and visual systems, we found that multisensory perception varied linearly with strength of correlation. Diffusion modelling confirmed this and revealed that correlation is supplied to the decisional system as sensory evidence. These data implicate correlation as an important cue in audiovisual feature integration and binding and suggest correlational strength as an important factor for flexibility in these processes.

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