Biology subjects

Pascucci, D.

Publications and source records attributed to Pascucci, D..

16 recordsLinked to original sources

bioRxiv2026

Not a single clock: multiple behavioral rhythms in perceptual averaging

Several theories propose that perception and attention are governed by rhythmic processes that give rise to periodic fluctuations in behavior. However, empirical support for behavioral rhythms has been derived largely from paradigms involving brief, static stimuli. Here, we introduce a temporal averaging task requiring integration of rapidly unfolding visual features. Across three experiments, we tested averaging of orientation, size, and color under different eccentricity conditions. We used a temporally weighted averaging model to assess whether the influence of individual stimulus samples on perceptual estimates exhibits periodic modulation over time. We found no common rhythmic signature across tasks. Instead, orientation and size judgments showed reliable low-frequency modulations (<2.5 Hz), whereas color judgments showed only weak trends. Higher-frequency components (~3.5-8 Hz), often linked to theta and alpha rhythms, were observed only in a subset of participants and were limited to parafoveal orientation processing. These findings challenge the notion of universal behavioral rhythms and instead suggest that temporal dynamics are task-dependent, with slow oscillatory processes emerging as the most consistent feature.

neuroscience↗

bioRxiv2026

Robust EEG brain-behavior associations emerge only at large sample sizes

Electroencephalography (EEG) offers unique access to the oscillatory dynamics that shape human cognition and behavior. Yet reported associations between EEG features and behavioral measures often fail to replicate, raising fundamental concerns about the robustness of this literature. A key reason is the predominance of small sample sizes (median [~]30 participants), but the consequences for reproducibility have not been systematically evaluated. Here, we provide the first large-scale empirical test of this key issue using the largest available harmonised resting-state EEG dataset, comprising 2,292 participants across healthy individuals and clinical populations. We extracted a comprehensive set of spectral, temporal, complexity, and dynamical features, and assessed their correlations with behavioral, cognitive, and mental health measures. Using extensive resampling across a wide range of sample sizes, we show that small samples produce unstable associations with inflated and inconsistent effect sizes, whereas larger cohorts converge on smaller but reproducible effects. These findings establish sample size as a critical determinant of reliability in EEG research and call for a re-evaluation of how oscillatory dynamics are linked to human cognition. By moving beyond low-powered correlations toward large-scale, reproducible approaches, the field will be better positioned to uncover the true role of neural oscillations in shaping behavior.

neuroscience↗

bioRxiv2026

The compositional nature of the human alpha rhythm

Resting-state brain activity is dominated by oscillations in the alpha band (8-13 Hz). The dominant frequency within this band, the individual alpha peak frequency (IAPF), has been widely used as a global marker of inter-individual variability in perceptual and cognitive functions, as well as clinical phenotypes. However, the traditional approach implicitly assumes a single oscillatory rhythm varying along a parametric continuum. Here, we analyzed resting-state EEG data from more than 2000 participants across multiple independent datasets. We identify three distinct and highly stable alpha components, or archetypes, that jointly and compositionally determine the IAPF. These components exhibit dissociable age-related trajectories as well as distinct scalp topographies and cortical sources. Together, these findings indicate that individual differences in alpha activity do not reflect variation along a single frequency dimension, but rather differences in the relative contribution of discrete alpha generators. This calls for a reevaluation of many reported associations between alpha frequency, brain function, and clinical phenotypes.

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bioRxiv2025

Response expectations shape serial dependence and stimulus processing

Perceptual decisions are biased by recent history, yet the balance between attractive and repulsive effects varies across contexts. Here, we tested whether trial-by-trial response expectations shape the direction of history biases in serial dependence during orientation reproduction. Behaviorally, we found that no-response trials--especially when rare--reduced attractive biases and enhanced repulsive biases. EEG results revealed stronger evoked responses and amplified neural representations for stimuli following no-response trials. Together, these findings suggest that interrupting the perception-action cycle fosters a state of re-engagement with current input and disengagement from past stimuli, indicating that serial dependence is a flexible process dynamically modulated by task expectations and transient shifts in sensory processing.

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bioRxiv2024

Neural correlates of unconscious and conscious visual processing

In some forms of postdictive phenomena, later events influence the perception of earlier ones, suggesting that conscious perception may be preceded by extended periods of unconscious processing. An example is the Sequential Metacontrast (SQM) paradigm, in which vernier offsets are unconsciously integrated over several hundred milliseconds before conscious perception. Obviously, the integrated percept can only emerge after each individual element in the stream has been processed. Thus, the SQM provides a unique opportunity to dissociate unconscious from conscious stages of visual processing, as these stages are well separated in time. Using EEG during the SQM, we identified two distinct stages of neural activity: an early occipital EEG activity pattern ([~]200 ms after the initial vernier) associated with unconscious processing, and a later centro-parietal EEG pattern ([~]400-600 ms after SQM onset) associated with the integrated percept and the behavioral report. We propose that the transition between these neural patterns marks the shift from unconscious encoding of individual visual stimuli to their integrated percept.

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bioRxiv2023

State-dependent serial dependence in perceptual decisions

Traditional views suggest that human perception handles uncertainty using optimal strategies. For instance, when prior stimuli are more reliable than current ones, perceptual decisions rely more on the past, leading to stronger serial dependence. Here, we report findings that challenge this view. We asked human observers to reproduce the average orientation of an ensemble of stimuli under varying stimulus uncertainty. Contrary to optimal strategies, we found that serial dependence is stronger when prior stimuli are more uncertain. We hypothesize that fluctuations in stimulus uncertainty may influence internal states of observers, such as participants expectations about uncertainty and beliefs about their own performance. A striking finding is that manipulating these internal states through rigged feedback can yield drastic effects on serial dependence, even when external input (i.e., stimulus uncertainty) remained constant. Our findings suggest that phenomena like serial dependence can be better understood by considering internal states of the observer, beyond fixed computations and optimal strategies.

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bioRxiv2022

Connectome spectrum electromagnetic tomography: a method to reconstruct electrical brain sources at high-spatial resolution

Connectome Spectrum Electromagnetic Tomography (CSET) combines diffusion MRI-derived structural connectivity data with well-established graph signal processing tools to solve the M/EEG inverse problem. Using simulated EEG signals from fMRI responses, and two EEG datasets on visual-evoked potentials, we provide evidence supporting that (i) CSET captures realistic neurophysiological patterns with better accuracy than state-of-the-art methods, (ii) CSET can reconstruct brain responses more accurately and with more robustness to intrinsic noise in the EEG signal. These results demonstrate that CSET offers high spatio-temporal accuracy, enabling neuroscientists to extend their research beyond the current limitations of low sampling frequency in functional MRI and the poor spatial resolution of M/EEG.

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bioRxiv2021

Feature distribution learning by passive exposure

Humans can rapidly estimate the statistical properties of groups of stimuli, including their average and variability. But recent studies of so-called Feature Distribution Learning (FDL) have shown that observers can quickly learn even more complex aspects of feature distributions. In FDL, observers learn the full shape of a distribution of features in a set of distractor stimuli and use this information to improve visual search: response times (RT) are slowed if the target feature lies inside the previous distractor distribution, and the RT patterns closely reflect the distribution shape. FDL requires only a few trials and is markedly sensitive to different distribution types. It is unknown, however, whether our perceptual system encodes feature distributions automatically and by passive exposure, or whether this learning requires active engagement with the stimuli. In two experiments, we sought to answer this question. During an initial exposure stage, participants passively viewed a display of 36 lines that included one orientation singleton or no singletons. In the following search display, they had to find an oddly oriented target. The orientations of the lines were determined either by a Gaussian or a uniform distribution. We found evidence for FDL only when the passive trials contained an orientation singleton. Under these conditions, RTs decreased as a function of the orientation distance between the target and the exposed distractor distribution. These results suggest that FDL can occur by passive exposure, but only if an orientation singleton appears during exposure to the distribution.

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bioRxiv2021

Structure supports function: informing directed and dynamic functional connectivity with anatomical priors

The dynamic repertoire of functional brain networks is constrained by the underlying topology of structural connections: the lack of a direct structural link between two brain regions prevents direct functional interactions. Despite the intrinsic relationship between structural (SC) and functional connectivity (FC), integrative and multimodal approaches to combine the two remain limited, especially for electrophysiological data. In the present work, we propose a new linear adaptive filter for estimating dynamic and directed FC using structural connectivity information as priors. We tested the filter in rat epicranial recordings and human event-related EEG data, using SC priors from a meta-analysis of tracer studies and diffusion tensor imaging metrics, respectively. Our results show that SC priors increase the resilience of FC estimates to noise perturbation while promoting sparser networks under biologically plausible constraints. The proposed filter provides intrinsic protection against SC-related false negatives, as well as robustness against false positives, representing a valuable new method for multimodal imaging and dynamic FC analysis.

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bioRxiv2021

Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes

We describe the multimodal neuroimaging dataset VEPCON (OpenNeuro Dataset ds003505). It includes raw data and derivatives of high-density EEG, structural MRI, diffusion weighted images (DWI) and single-trial behavior (accuracy, reaction time). Visual evoked potentials (VEPs) were recorded while participants (n=20) discriminated briefly presented faces from scrambled faces, or coherently moving stimuli from incoherent ones. EEG and MRI were recorded separately from the same participants. The dataset contains pre-processed EEG of single trials in each condition, behavioral measures, structural MRIs, individual brain parcellations at 5 spatial resolutions (83 to 1015 regions), and the corresponding structural connectomes computed from fiber count, fiber density, average fractional anisotropy and mean diffusivity maps. For source imaging, VEPCON provides EEG inverse solutions based on individual anatomy, with Python and Matlab scripts to derive activity time-series in each brain region, for each parcellation level. The BIDS-compatible dataset can contribute to multimodal methods development, studying structure-function relations, and to unimodal optimization of source imaging and graph analyses, among many other possibilities.

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bioRxiv2021

The connectome spectrum as a canonical basis for a sparse representation of fast brain activity

The functional organization of neural processes is constrained by the brain s intrinsic structural connectivity. Here, we explore the potential of exploiting this structure in order to improve the signal representation properties of brain activity and its dynamics. Using a multi-modal imaging dataset (electroencephalography, structural MRI and diffusion MRI), we represent electrical brain activity at the cortical surface as a time-varying composition of harmonic modes of structural connectivity. The harmonic modes are termed connectome harmonics, and their representation is known as the connectome spectrum of the signal. We found that: first, the brain activity signal is more compactly represented by the connectome spectrum than by the traditional area-based representation; second, the connectome spectrum characterizes fast brain dynamics in terms of signal broadcasting profile, revealing different temporal regimes of integration and segregation that are consistent across participants. And last, the connectome spectrum characterises fast brain dynamics with fewer degrees of freedom than area-based signal representations. Specifically, we show that with the connectome spectrum representation, fewer dimensions are needed to capture the differences between low-level and high-level visual processing, and the topological properties of the signal. In summary, this work provides statistical, functional and topological evidence supporting that by accounting for the brain s structural connectivity fosters a more comprehensive understanding of large-scale dynamic neural functioning.

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bioRxiv2020

Connectome spectral analysis to track EEG task dynamics on a subsecond scale

We present an approach for tracking fast spatiotemporal cortical dynamics in which we combine white matter connectivity data with source-projected electroencephalographic (EEG) data. We employ the mathematical framework of graph signal processing in order to derive the Fourier modes of the brain structural connectivity graph, or "network harmonics". These network harmonics are naturally ordered by smoothness. Smoothness in this context can be understood as the amount of variation along the cortex, leading to a multi-scale representation of brain connectivity. We demonstrate that network harmonics provide a sparse representation of the EEG signal, where, at certain times, the smoothest 15 network harmonics capture 90% of the signal power. This suggests that network harmonics are functionally meaningful, which we demonstrate by using them as a basis for the functional EEG data recorded from a face detection task. There, only 13 network harmonics are sufficient to track the large-scale cortical activity during the processing of the stimuli with a 50 ms resolution, reproducing well-known activity in the fusiform face area as well as revealing co-activation patterns in somatosensory/motor and frontal cortices that an unconstrained ROI-by-ROI analysis fails to capture. The proposed approach is simple and fast, provides a means of integration of multimodal datasets, and is tied to a theoretical framework in mathematics and physics. Thus, network harmonics point towards promising research directions both theoretically - for example in exploring the relationship between structure and function in the brain - and practically - for example for network tracking in different tasks and groups of individuals, such as patients.

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bioRxiv2020

Nested oscillations and brain connectivity during sequential stages of feature-based attention

Brain mechanisms of visual selective attention involve both local and network-level activity changes at specific oscillatory rhythms, but their interplay remains poorly explored. Here, we investigate anticipatory and reactive effects of feature-based attention using separate fMRI and EEG recordings, while participants attended to one of two spatially overlapping visual features (motion and orientation). We focused on EEG source analysis of local nested oscillations and on graph analysis of connectivity changes in a network of fMRI-defined regions of interest, and characterized a cascade of attentional effects and their interplay at multiple spatial scales. We discuss how the results may reconcile several theories of selective attention, by showing how {beta} rhythms support anticipatory information routing through increased network efficiency and {beta}-{gamma} coupling in functionally specialized regions (V1 for orientation, V5 for motion), while reactive -band desynchronization patterns and increased -{gamma} coupling in V1 and V5 mediate stimulus-evoked processing of task-relevant signals.

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bioRxiv2019

Modeling time-varying brain networks with a self-tuning optimized Kalman filter

Brain networks are complex dynamical systems in which directed interactions between different areas evolve at the sub-second scale of sensory, cognitive and motor processes. Due to the highly non-stationary nature of neural signals and their unknown noise components, however, modeling dynamic brain networks has remained one of the major challenges in contemporary neuroscience. Here, we present a new algorithm based on an innovative formulation of the Kalman filter that is optimized for tracking rapidly evolving patterns of directed functional connectivity under unknown noise conditions. The Self-Tuning Optimized Kalman filter (STOK) is a novel adaptive filter that embeds a self-tuning memory decay and a recursive regularization to guarantee high network tracking accuracy, temporal precision and robustness to noise. To validate the proposed algorithm, we performed an extensive comparison against the classical Kalman filter, in both realistic surrogate networks and real electroencephalography (EEG) data. In both simulations and real data, we show that the STOK filter estimates time-frequency patterns of directed connectivity with significantly superior performance. The advantages of the STOK filter were even clearer in real EEG data, where the algorithm recovered latent structures of dynamic connectivity from epicranial EEG recordings in rats and human visual evoked potentials, in excellent agreement with known physiology. These results establish the STOK filter as a powerful tool for modeling dynamic network structures in biological systems, with the potential to yield new insights into the rapid evolution of network states from which brain functions emerge. Author summaryDuring normal behavior, brains transition between functional network states several times per second. This allows humans to quickly read a sentence, and a frog to catch a fly. Understanding these fast network dynamics is fundamental to understanding how brains work, but up to now it has proven very difficult to model fast brain dynamics for various methodological reasons. To overcome these difficulties, we designed a new Kalman filter (STOK) by innovating on previous solutions from control theory and state-space modelling. We show that STOK accurately models fast network changes in simulations and real neural data, making it an essential new tool for modelling fast brain networks in the time and frequency domain.

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bioRxiv2017

Gating by induced α-γ asynchrony in selective attention

Visual selective attention operates through top-down mechanisms of signal enhancement and suppression, mediated by -band oscillations. The effects of such top-down signals on local processing in primary visual cortex (V1) remain poorly understood. In the present work, we characterize the interplay between large-scale interactions and local activity changes in V1 that orchestrates selective attention, using Granger-causality and phase-amplitude coupling (PAC) analysis of EEG source signals. The task required participants to either attend to or ignore oriented gratings. Results from time-varying, directed connectivity analysis revealed frequency specific effects of attentional selection: bottom-up {gamma}-band influences from visual areas increased rapidly in response to attended stimuli while distributed top-down -band influences originated from parietal cortex in response to ignored stimuli. Importantly, the results revealed a critical interplay between top-down parietal signals and -{gamma} PAC in visual areas. Parietal -band influences disrupted the -{gamma} coupling in visual cortex, which in turn reduced the amount of {gamma}-band outflow from visual areas. Our results are a first demonstration of how directed interactions affect cross-frequency coupling in downstream areas depending on task demands. These findings suggest that parietal cortex realizes selective attention by disrupting cross-frequency coupling at target regions, which prevents them from propagating task-irrelevant information.

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bioRxiv2017

Laws of concatenated perception: Vision goes for novelty, Decisions for perseverance

Every instant of perception depends on a cascade of brain processes calibrated to the history of sensory and decisional events. In the present work, we show that human visual perception is constantly shaped by two contrasting forces, exerted by sensory adaptation and past decisions. In a series of experiments, we used multilevel modelling and cross-validation approaches to investigate the impact of previous stimuli and responses on current errors in adjustment tasks. Our results revealed that each perceptual report is permeated by opposite biases from a hierarchy of serially dependent processes: low-level adaptation repels perception away from previous stimuli; high-level, decisional traces attract perceptual reports toward previous responses. Contrary to recent claims, we demonstrated that positive serial dependence does not result from continuity fields operating at the level of early visual processing, but arises from the inertia of decisional templates. This finding is consistent with a Two-process model of serial dependence in which the persistence of read-out weights in a decision unit compensates for sensory adaptation, leading to attractive biases in sequential responses. We propose the first unified account of serial dependence in which functionally distinct mechanisms, operating at different stages, promote the differentiation and integration of visual information over time.

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