Biology subjects

Pantazis, D.

Publications and source records attributed to Pantazis, D..

At least 19 recordsLinked to original sources

bioRxiv2025

Localization of Realistic Spatial Patches of Complex Source Activity in MEG

Accurate localization of neural sources in Magnetoencephalography (MEG) and Electroencephalography (EEG) is essential for advancing clinical and research applications in neuroscience. Traditional approaches like dipole fitting (e.g., MUSIC, RAP-MUSIC) are limited to discrete focal sources, while distributed source imaging methods (e.g., MNE, sLORETA) assume sources distributed across the cortical surface. These methods, however, often fail to capture sources with complex spatial extents, limiting their accuracy in realistic settings. To address these limitations, we introduce PATCH-AP, an enhanced version of the Alternating Projection (AP) method that effectively localizes both discrete and spatially extended sources. We evaluated PATCH-AP against leading source localization methods, including distributed source imaging techniques (MNE, sLORETA), traditional dipole fitting (AP), and recent extended source methods (Convexity-Champagne (CC), FLEX-AP). PATCH-AP consistently outperformed these methods in simulations, achieving lower Earth Movers Distance (EMD) scores--a metric indicating closer alignment with the true source distribution. In tests with real MEG data from a face perception task, PATCH-AP demonstrated high alignment with the fusiform face area, a region critical for face processing. These results highlight PATCH-APs potential to enhance source localization accuracy, promising significant advancements in neuroscience research and clinical diagnostics.

neuroscience↗

bioRxiv2025

Spatiotemporal Dynamics of Invariant Face Representations in the Human Brain

The human brain can effortlessly extract a familiar faces age, gender, and identity despite dramatic changes in appearance, such as head orientation, lighting, or expression. Yet, the spatiotemporal dynamics underlying this ability, and how they depend on task demands, remain unclear. Here, we used multivariate decoding of magnetoencephalography (MEG) responses and source localization to characterize the emergence of invariant face representations. Human participants viewed natural images of highly familiar celebrities that systematically varied in viewpoint, gender, and age, while performing a one-back task on the identity or the image. Time-resolved decoding revealed that identity information emerged rapidly and became increasingly invariant to viewpoint over time. We observed a temporal hierarchy: view-specific identity information appeared at 64 ms, followed by mirror-invariant representations at 75 ms and fully view-invariant identity at 89 ms. Identity-invariant age and gender information emerged around the same time as view-invariant identity. Task demands modulated only late-stage identity and gender representations, suggesting that early face processing is predominantly feedforward. Source localization at peak decoding times showed consistent involvement of the occipital face area (OFA) and fusiform face area (FFA), with stronger identity and age signals than gender. Our findings reveal the spatiotemporal dynamics by which the brain extracts view-invariant identity from familiar faces, suggest that age and gender are processed in parallel, and show that task demands modulate later processing stages. Together, these results offer new constraints on computational models of face perception.

neuroscience↗

bioRxiv2024

Touch to text: Spatiotemporal evolution of braille letter representations in blind readers

Visual deprivation does not silence the visual cortex, which is responsive to auditory, tactile, and other nonvisual tasks in blind persons. However, the underlying functional dynamics of the neural networks mediating such crossmodal responses remain unclear. Here, using braille reading as a model framework to investigate these networks, we presented sighted (N=13) and blind (N=12) readers with individual visual print and tactile braille alphabetic letters, respectively, during MEG recording. Using time-resolved multivariate pattern analysis and representational similarity analysis, we traced the alphabetic letter processing cascade in both groups of participants. We found that letter representations unfolded more slowly in blind than in sighted brains, with decoding peak latencies [~]200 ms later in braille readers. Focusing on the blind group, we found that the format of neural letter representations transformed within the first 500 ms after stimulus onset from a low-level structure consistent with peripheral nerve afferent coding to high-level format reflecting pairwise letter embeddings in a text corpus. The spatiotemporal dynamics of the transformation suggest that the processing cascade proceeds from a starting point in somatosensory cortex to early visual cortex and then to inferotemporal cortex. Together our results give insight into the neural mechanisms underlying braille reading in blind persons and the dynamics of functional reorganization in sensory deprivation.

neuroscience↗

bioRxiv2024

Fully Hyperbolic Neural Networks: A Novel Approach to Studying Aging Trajectories

Characterizing age-related alterations in brain networks is crucial for understanding aging trajectories and identifying deviations indicative of neurodegenerative disorders, such as Alzheimers disease. In this study, we developed a Fully Hyperbolic Neural Network (FHNN) to embed functional brain connectivity graphs derived from magnetoencephalography (MEG) data into low dimensions on a Lorentz model of hyperbolic space. Using this model, we computed hyperbolic embeddings of the MEG brain networks of 587 individuals from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) dataset. Notably, we leveraged a unique metric--the radius of the node embeddings--which effectively captures the hierarchical organization of the brain, to characterize subtle hierarchical organizational changes in various brain subnetworks attributed to the aging process. Our findings revealed that a considerable number of subnetworks exhibited a reduction in hierarchy during aging, with some showing gradual changes and others undergoing rapid transformations in the elderly. Moreover, we demonstrated that hyperbolic features outperform traditional graph-theoretic measures in capturing age-related information in brain networks. Overall, our study represents the first evaluation of hyperbolic embeddings in MEG brain networks for studying aging trajectories, shedding light on critical regions undergoing significant age-related alterations in the large cohort of the Cam-CAN dataset.

bioinformatics↗

bioRxiv2023

Localization of Spatially Extended Brain Sources by Flexible Alternating Projections (FLEX-AP)

Magnetoencephalography (MEG) and electroencephalography (EEG) are widely employed techniques for the in-vivo measurement of neural activity with exceptional temporal resolution. Modeling the neural sources underlying these signals is of high interest for both neuroscience research and pathology. The method of Alternating Projection (AP) was recently shown to outperform the well-established recursively applied and projected multiple signal classification (RAP-MUSIC) algorithm. In this work, we further enhanced AP to allow for source extent estimation, a novel approach termed flexible extent AP (FLEX-AP). We found that FLEX-AP achieves significantly lower errors for spatially coherent sources compared to AP, RAP-MUSIC, and the corresponding extension, FLEX-RAP-MUSIC. We also found an advantage for discrete dipoles under forward modeling errors encountered in real-world scenarios. Together, our results indicate that the FLEX-AP method can unify dipole fitting and distributed source imaging into a single algorithm with promising accuracy.

neuroscience↗

bioRxiv2023

Hyperbolic graph embedding of MEG brain networks to study brain alterations in individuals with subjective cognitive decline

An expansive area of research focuses on discerning patterns of alterations in functional brain networks from the early stages of Alzheimers disease, even at the subjective cognitive decline (SCD) stage. Here, we developed a novel hyperbolic MEG brain network embedding framework for transforming high-dimensional complex MEG brain networks into lower-dimensional hyperbolic representations. Using this model, we computed hyperbolic embeddings of the MEG brain networks of two distinct participant groups: individuals with SCD and healthy controls. We demonstrated that these embeddings preserve both local and global geometric information, presenting reduced distortion compared to rival models, even when brain networks are mapped into low-dimensional spaces. In addition, our findings showed that the hyperbolic embeddings encompass unique SCD-related information that improves the discriminatory power above and beyond that of connectivity features alone. Notably, we introduced a unique metric--the radius of the node embeddings--which effectively proxies the hierarchical organization of the brain. Using this metric, we identified subtle hierarchy organizational differences between the two participant groups, suggesting increased hierarchy in the dorsal attention, frontoparietal, and ventral attention subnetworks among the SCD group. Last, we assessed the correlation between these hierarchical variations and cognitive assessment scores, revealing associations with diminished performance across multiple cognitive evaluations in the SCD group. Overall, this study presents the first evaluation of hyperbolic embeddings of MEG brain networks, offering novel insights into brain organization, cognitive decline, and potential diagnostic avenues of Alzheimers disease.

bioinformatics↗

bioRxiv2023

Functional identification of language-responsive channels in individual participants in MEG investigations

Making meaningful inferences about the functional architecture of the language system requires the ability to refer to the same neural units across individuals and studies. Traditional brain imaging approaches align and average brains together in a common space. However, lateral frontal and temporal cortices, where the language system resides, is characterized by high structural and functional inter-individual variability, which reduces the sensitivity and functional resolution of group-averaging analyses. This issue is compounded by the fact that language areas lay in close proximity to regions of other large-scale networks with different functional profiles. A solution inspired by visual neuroscience is to identify language areas functionally in each individual brain using a localizer task (e.g., a language comprehension task). This approach has proven productive in fMRI, yielding a number of robust and replicable findings about the language system. Here, we extend this approach to MEG. Across two experiments (one in Dutch speakers, n=19; one in English speakers, n=23), we examined neural responses to the processing of sentences and a control condition (nonword sequences). In both the time and frequency domains, we demonstrated that the topography of neural responses to language is spatially stable within individuals but varies across individuals. Consequently, analyses that take this inter-individual variability into account are characterized by greater sensitivity, compared to the group-level analyses. In summary, similar to fMRI, functional identification within individuals yields benefits in MEG, thus opening the door to future investigations of language processing including questions where whole-brain coverage and temporal resolution are both critical.

neuroscience↗

bioRxiv2022

Hierarchical whole-brain dynamics of auditory predictive coding at the fine-grained milliseconds level

Our brain is constantly extracting, predicting, and recognising key spatiotemporal features of the physical world in order to survive. While neural processing of visuospatial patterns has been extensively studied, the hierarchical brain mechanisms underlying conscious recognition of auditory sequences and the associated prediction errors remain elusive. Using magnetoencephalography (MEG), we studied the brain functioning of 83 participants during recognition of previously memorised musical sequences and systematic variations. The results showed feedforward connections originating from auditory cortices, and extending to the hippocampus, anterior cingulate gyrus, and medial cingulate gyrus. Simultaneously, we observed backward connections operating in the opposite direction. Throughout the sequences, the hippocampus and cingulate gyrus maintained the same hierarchical level, except for the final tone, where the cingulate gyrus assumed the top position within the hierarchy. The evoked responses of memorised sequences and variations engaged the same hierarchical brain network but systematically differed in terms of temporal dynamics, strength, and polarity. Furthermore, induced-response analysis showed that alpha and beta power was stronger for the variations, while gamma power was enhanced for the memorised sequences. This study expands on the predictive coding theory by providing quantitative evidence of hierarchical brain mechanisms during conscious memory and predictive processing of auditory sequences.

neuroscience↗

bioRxiv2022

Why Are Alzheimer's Disease Patients Unaware of their Memory Deficits?

Anosognosia, or the lack of awareness of ones own impairment, is frequent for memory deficits in patients with Alzheimers disease (AD). Although often related to frontal dysfunctions, the neural mechanisms of anosognosia remain largely unknown. We hypothesized that anosognosia in AD may result from a failure in the error-monitoring system, thus preventing patients from being aware of and learning from their own errors. We therefore investigated the event-related potentials evoked by erroneous responses during a memory task in two groups of amyloid positive individuals who had only subjective memory complaints at study entry: 1) those who progressed to AD; and 2) those who remained cognitively normal after five years of follow-up. Our findings revealed direct evidence of a failure in the error-monitoring system at early stages of AD, suggesting that it may be the critical neural substrate of anosognosia in this neurodegenerative disorder.

neuroscience↗

bioRxiv2021

Temporal pattern recognition in the human brain: a dual simultaneous processing

Pattern recognition is a major scientific topic. Strikingly, while machine learning algorithms are constantly refined, the human brain emerges as an ancestral biological example of such complex procedure. However, how it transforms sequences of single objects into meaningful temporal patterns remains elusive. Using magnetoencephalography (MEG) and magnetic resonance imaging (MRI), we discovered and mathematically modelled an inedited dual simultaneous processing responsible for pattern recognition in the brain. Indeed, while the objects of the temporal pattern were independently elaborated by a local, rapid brain processing, their combination into a meaningful superordinate pattern depended on a concurrent global, slower processing involving a widespread network of sequentially active brain areas. Expanding the established knowledge of neural information flow from low- to high-order brain areas, we revealed a novel brain mechanism based on simultaneous activity in different frequency bands within the same brain regions, highlighting its crucial role underlying complex cognitive functions.

neuroscience↗

bioRxiv2020

Uncovering the geometry of color space with magnetoencephalography (MEG)

The geometry that describes the relationship among colors is unsettled despite centuries of study. Here we present a new approach, using multivariate analyses of direct measurements of brain activity obtained with magnetoencephalography to reverse-engineer the geometry of the neural representation of color space. The analyses depend upon determining similarity relationships among the neural responses to different colors and assessing how these relationships change in time. To evaluate the approach, we relate patterns of neural activity to universal patterns in color naming. Control experiments showed that responses to color words could not decode activity elicited by color stimuli. The results suggest that three patterns of color naming can be accounted for by decoding the similarity relationships in the neural representation of color: the association of warm colors such as reds and oranges with "light" and cool colors such as blues and greens with "dark"; the greater precision among all languages in naming warm colors compared to cool colors; and the preeminence of red.

neuroscience↗

bioRxiv2020

Spatiotemporal brain dynamics during recognition of the music of Johann Sebastian Bach

Music is a non-verbal human language, built on logical structures and articulated in balanced hierarchies between sounds, offering excellent opportunities to explore how the brain creates meaning for complex spatiotemporal auditory patterns. Using the high temporal resolution of magnetoencephalography in 70 participants, we investigated their unfolding brain dynamics during the recognition of previously memorized J.S. Bachs musical patterns from prelude in C minor BWV 847 compared to novel patterns matched in terms of entropy and information content. Remarkably, the recognition of the memorized music ignited a widespread brain network comprising primary auditory cortex, superior temporal gyrus, insula, frontal operculum, cingulate gyrus, orbitofrontal cortex, basal ganglia, thalamus and hippocampus. Furthermore, measures of both brain activity and functional connectivity presented an overall increase over time, following the evolution and unfolding of the memorized musical patterns. Specifically, while the auditory cortex responded mainly to the first tones of the patterns, the activity and synchronization of higher-order brain areas such as cingulate, frontal operculum, hippocampus and orbitofrontal cortex largely increased over time, arguably representing the key whole-brain mechanisms for conscious recognition of auditory patterns as predicted by the global neuronal workspace hypothesis. In conclusion, our study described the fine-grained whole-brain activity and functional connectivity dynamics responsible for processing and recognition of previously memorized music. Further, the study highlights how the use of musical patterns in combination with a wide array of analytical tools and neuroscientific measures spanning from decoding to fast neural phase synchronization can shed new light on meaningful, complex cognitive processes.

neuroscience↗

bioRxiv2020

Temporal dynamics of the neural mechanisms for encoding hue and luminance contrast uncovered by magnetoencephalography

Hue and luminance contrast are basic visual features, yet the timing of the neural computations that extract them, and whether they depend on common neural circuits, is not well established. Using multivariate analyses of magnetoencephalography data, we show that hue and luminance-contrast polarity can be decoded from MEG data and, with lower accuracy, both features can be decoded across changes in the other feature. These results are consistent with the existence of both common and separable neural mechanisms. The decoding time course is earlier and more temporally precise for luminance polarity than hue, a result that does not appear to depend on task, suggesting that luminance contrast is an updating signal that separates visual events. Meanwhile, cross-temporal generalization is slightly greater for representations of hue compared to luminance polarity, providing a neural correlate of the preeminence of hue in perceptual grouping and memory. Finally, decoding of luminance polarity varies depending on the hues used to obtain training and testing data; the pattern of results suggests that luminance contrast is mediated by both L-M and S cone sub-cortical mechanisms.

neuroscience↗

bioRxiv2019

Gaussian embedding-based functional brain connectomic analysis for amnestic mild cognitive impairment patients with cognitive training

Identifying heterogeneous cognitive impairment markers at an early stage is vital for Alzheimers disease diagnosis. However, due to complex and uncertain brain connectivity features in the cognitive domains, it remains challenging to quantify functional brain connectomic changes during non-pharmacological interventions for amnestic mild cognitive impairment (aMCI) patients. We present a new quantitative functional brain network analysis of fMRI data based on the multi-graph unsupervised Gaussian embedding method (MG2G). This neural network-based model can effectively learn low-dimensional Gaussian distributions from the original high-dimensional sparse functional brain networks, quantify uncertainties in link prediction, and discover the intrinsic dimensionality of brain networks. Using the Wasserstein distance to measure probabilistic changes, we discovered that brain regions in the default mode network and somatosensory/somatomotor hand, fronto-parietal task control, memory retrieval, and visual and dorsal attention systems had relatively large variations during non-pharmacological training, which might provide distinct biomarkers for fine-grained monitoring of aMCI cognitive alteration.

neuroscience↗

bioRxiv2019

The speed of human social interaction perception

The ability to detect and understand other peoples social interactions is a fundamental part of the human visual experience that develops early in infancy and is shared with other primates. However, the neural computations underlying this ability remain largely unknown. Is the detection of social interactions a rapid perceptual process, or a slower post-perceptual inference? Here we used magnetoencephalography (MEG) decoding and computational modeling to ask whether social interactions can be detected via fast, feedforward processing. Subjects in the MEG viewed snapshots of visually matched real-world scenes containing a pair of people who were either engaged in a social interaction or acting independently. The presence versus absence of a social interaction could be read out from subjects MEG data spontaneously, even while subjects performed an orthogonal task. This readout generalized across different scenes, revealing abstract representations of social interactions in the human brain. These representations, however, did not come online until quite late, at 300 ms after image onset, well after the time period of feedforward visual processes. In a second experiment, we found that social interaction readout occurred at this same latency even when subjects performed an explicit task detecting social interactions. Consistent with these latency results, a standard feedforward deep neural network did not contain an abstract representation of social interactions at any model layer. We further showed that MEG responses distinguished between different types of social interactions (mutual gaze vs joint attention) even later, around 500 ms after image onset. Taken together, these results suggest that the human brain spontaneously extracts the presence and type of others social interactions, but does so slowly, likely relying on iterative top-down computations.

neuroscience↗

bioRxiv2018

How face perception unfolds over time

Within a fraction of a second of viewing a face, we have already determined its gender, age and identity. A full understanding of this remarkable feat will require a characterization of the computational steps it entails, along with the representations extracted at each. To this end, we used magnetoencephalography to measure the time course of neural responses to faces, thereby addressing two fundamental questions about how face processing unfolds over time. First, using representational similarity analysis, we found that facial gender and age information emerged before identity information, suggesting a coarse-to-fine processing of face dimensions. Second, identity and gender representations of familiar faces were enhanced very early on, indicating that the previously-reported behavioral benefit for familiar faces results from tuning of early feed-forward processing mechanisms. These findings start to reveal the time course of face perception in humans, and provide powerful new constraints on computational theories of face perception.

neuroscience↗

bioRxiv2018

The Perceptual Neural Trace of Memorable Unseen Scenes

Some scenes are more memorable than others: they cement in minds with consistencies across observers and time scales. While memory mechanisms are traditionally associated with the end stages of perception, recent behavioral studies suggest that the features driving these memorability effects are extracted early on, and in an automatic fashion. This raises the question: is the neural signal of memorability detectable during early perceptual encoding phases of visual processing? Using the high temporal resolution of magnetoencephalography (MEG), during a rapid serial visual presentation (RSVP) task, we traced the neural temporal signature of memorability across the brain. We found an early and prolonged memorability related signal recruiting a network of regions in both dorsal and ventral streams, detected outside of the constraints of subjective awareness. This enhanced encoding could be the key to successful storage and recognition.

neuroscience↗

bioRxiv2018

Ultra-Rapid Serial Visual Presentation Reveals Dynamics of Feedforward and Feedback Processes in the Ventral Visual Pathway

Human visual recognition activates a dense network of overlapping feedforward and recurrent neuronal processes, making it hard to disentangle processing in the feedforward from the feedback direction. Here, we used ultra-rapid serial visual presentation to suppress sustained activity that blurs the boundaries of processing steps, enabling us to resolve two distinct stages of processing with MEG multivariate pattern classification. The first processing stage was the rapid activation cascade of the bottom-up sweep, which terminated early as visual stimuli were presented at progressively faster rates. The second stage was the emergence of categorical information with peak latency that shifted later in time with progressively faster stimulus presentations, indexing time-consuming recurrent processing. Using MEG-fMRI fusion with representational similarity, we localized recurrent signals in early visual cortex. Together, our findings segregated an initial bottom-up sweep from subsequent feedback processing, and revealed the neural signature of increased recurrent processing demands for challenging viewing conditions.

neuroscience↗