Biology subjects

Tovar, D. A.

Publications and source records attributed to Tovar, D. A..

6 recordsLinked to original sources

bioRxiv2025

The brain dynamics of congenitally blind people seeing faces with sound

Sensory substitution devices (SSDs) convert images to sounds to equip blind individuals with nominally visual functions, like face or letter sensitivity. Prior studies showed that image-to-sound SSDs engage cortices ordinarily specialised for visual functions. However, the brain dynamics of SSD-supported perception remains unknown. Either visual cortices are the first locus of discrimination of SSD percepts, or their activation is a byproduct of perceptual processes unfurling elsewhere, such as in auditory cortices. Resolving this uncertainty is critical for understanding the contribution of visual cortices to mechanisms subserving SSD-induced perceptions in the blind. Using electrical neuroimaging of EEG data from congenitally blind adults, we show for the first time that visual cortices are the earliest site of face and letter sensitivity when conveyed via SSDs, though with distinct temporal dynamics and spatial localizations. In the case of faces versus scrambled faces, differences first manifested at 460ms post-stimulus onset as topographic EEG modulations that were in turn localized to a network of right-hemisphere regions, including the fusiform face area (FFA) as well as lateral occipital cortices. In the case of discerning vertically versus horizontally oriented letter shapes, differences first manifested at 370ms post-stimulus onset as topographic EEG modulations that were localized to a network of left-hemisphere regions, including the occipital pole and the occipito-temporal junction extending into the angular gyrus. Notably, early-latency auditory evoked potentials did not differ based on visual properties of the soundscapes. These collective data support the proposition that responses to soundscapes are routed to brain circuits ordinarily specialized for processing visual information and that these circuits are the loci of the initial discriminant processes. By providing the temporal dynamics of SSD perception, our findings provide unique evidence for the theory that cortices are characterised by task-contingent functional organisation. HighlightsO_LISensory substitution devices (SSDs) provide the blind access to visual information C_LIO_LIBrain dynamics in visual cortices to SSD-conveyed object images are unknown C_LIO_LIEarly EEG sensitivity for faces and letters was localized to the visual cortex C_LIO_LIThe blind visual cortex takes part in early SSD processing for visual categories C_LI eTOC blurbWe characterise for the first time the brain dynamics of the blind using soundscapes for face and letter discrimination. Faces recruited the FFA at 460ms post-stimulus onset, whereas letters recruited the occipital pole, occipito-temporal junction, and angular gyrus at 370ms post-stimulus onset. Our findings of the temporal dynamics of SSD perception uniquely support the theory that cortices are characterised by task-selective sensory-independent functional organisation.

neuroscience↗

bioRxiv2023

Near-field potentials index local neural computations more accurately than population spiking

Local field potentials (LFP) are low-frequency extracellular voltage fluctuations thought to primarily arise from synaptic activity. However, unlike highly localized neuronal spiking, LFP is spatially less specific. LFP measured at one location is not entirely generated there due to far-field contributions that are passively conducted across volumes of neural tissue. We sought to quantify how much information within the locally generated, near-field low-frequency activity (nfLFP) is masked by volume-conducted far-field signals. To do so, we measured laminar neural activity in primary visual cortex (V1) of monkeys viewing sequences of multifeatured stimuli. We compared information content of regular LFP and nfLFP that was mathematically stripped of volume-conducted far-field contributions. Information content was estimated by decoding stimulus properties from neural responses via spatiotemporal multivariate pattern analysis. Volume-conducted information differed from locally generated information in two important ways: (1) for stimulus features relevant to V1 processing (orientation and eye-of-origin), nfLFP contained more information. (2) in contrast, the volume-conducted signal was more informative regarding temporal context (relative stimulus position in a sequence), a signal likely to be coming from elsewhere. Moreover, LFP and nfLFP differed both spectrally as well as spatially, urging caution regarding the interpretations of individual frequency bands and/or laminar patterns of LFP. Most importantly, we found that population spiking of local neurons was less informative than either the LFP or nfLFP, with nfLFP containing most of the relevant information regarding local stimulus processing. These findings suggest that the optimal way to read out local computational processing from neural activity is to decode the local contributions to LFP, with significant information loss hampering both regular LFP and local spiking. Authors ContributionsConceptualization, D.A.T., J.A.W, and A.M.; Data Collection, J.A.W., M.A.C., K.D.; Formal Analysis, D.A.T. and J.A.W.; Data Visualization, D.A.T. and J.A.W.; Original Draft, D.A.T., J.A.W., and A.M.; Revisions and Final Draft, D.A.T., J.A.W., M.A.C., K.D., M.T.W., A.M.B., and A.M. Competing InterestsThe authors declare no conflicts of interest.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗