Biology subjects

da Silva Castanheira, J.

Publications and source records attributed to da Silva Castanheira, J..

12 recordsLinked to original sources

bioRxiv2026

Unsupervised Representation Learning Generates Differentiable Neurophysiological Profiles

Human brain activity contains stable, individual-specific features that persist over months to years, forming neurophysiological profiles. Most model-based profiling approaches use participant labels or supervised objectives, making it difficult to determine whether successful differentiation reflects stable biology or exploitable idiosyncrasies. We introduce a participant-agnostic autoencoder framework that derives profiles from brief resting-state magnetoencephalography (MEG) segments using reconstruction as sole training objective. Discriminative profiles emerged from the learned latent space without participant labels. Within-session, autoencoder profiles reached 93.3% accuracy at 120 s, exceeding functional-connectivity, spectral, and contrastive baselines with recordings as short as 14 s when participant-specific anatomy was withheld from source reconstruction. Differentiation generalized above chance across recording sessions (between-session accuracy 49.5% for the pretrained autoencoder). Profiles also predicted age more accurately than baselines (r2=0.318), and the decoder enabled perturbation-based sensitivity analyses in spectral and connectivity spaces. This establishes participant-agnostic representation learning as a scalable and interpretable profiling.

neuroscience↗

bioRxiv2025

Quantifying Rhythmic and Arrhythmic Components of Brain Activity

Brain activity comprises both rhythmic (periodic) and arrhythmic (aperiodic) components. These signal elements vary across healthy aging, and disease, and may make distinct contributions to conscious perception. Despite pioneering techniques to parameterize rhythmic and arrhythmic neural components based on power spectra, the methodology for quantifying rhythmic activity remains in its infancy. Previous work has relied on parametric estimates of rhythmic power extracted from specparam, or estimates of rhythmic power obtained after detrending neural spectra. Variation in analytical choices for isolating brain rhythms from background arrhythmic activity makes interpreting findings across studies difficult. Whether these current approaches can accurately recover the independent contribution of these neural signal elements remains to be established. Here, using simulation and parameter recovery approaches, we show that power estimates obtained from detrended spectra conflate these two neurophysiological components, yielding spurious correlations between spectral model parameters. In contrast, modelled rhythmic power obtained from specparam, which detrends the power spectra and parametrizes brain rhythms, independently recovers the rhythmic and arrhythmic components in simulated neural time series, minimising spurious relationships. We validate these methods using resting-state recordings from a large cohort. Based on our findings, we recommend modelled rhythmic power estimates from specparam for the robust independent quantification of rhythmic and arrhythmic signal components for cognitive neuroscience.

neuroscience↗

bioRxiv2024

The Lifespan Evolution of Individualized Neurophysiological Traits

How do neurophysiological traits that characterize individuals evolve across the lifespan? To address this question, we analyzed brief, task-free magnetoencephalographic recordings from over 1,000 individuals aged 4-89. We found that neurophysiological activity is significantly more similar between individuals in childhood than in adulthood, though periodic patterns of brain activity remain reliable markers of individuality across all ages. The cortical regions most critical for determining individuality shift across neurodevelopment and aging, with sensorimotor cortices becoming increasingly prominent in adulthood. These developmental changes in neurophysiology align closely with the expression of cortical genetic systems related to ion transport and neurotransmission, suggesting a growing influence of genetic factors on neurophysiological traits across the lifespan. Notably, this alignment peaks in late adolescence, a critical period when genetic factors significantly shape brain individuality. Overall, our findings highlight the role of sensorimotor regions in defining individual brain traits and reveal how genetic influences on these traits intensify with age. This study advances our understanding of the evolving biological foundations of inter-individual differences. Lay summaryThis study examines how brain activity reflects the development of individuality across a persons life. Using magnetoencephalography to capture brief recordings of spontaneous brain activity, the researchers distinguished between over 1,000 individuals, spanning ages 4 to 89. They found that the brain regions most associated with individuality change with age: sensory and motor regions become increasingly distinctive in early adulthood, highlighting their role in shaping a persons unique characteristics of brain activity. The study also revealed that changes in brain activity across different ages correspond to specific patterns of gene expression, shedding light on how genetics influence brain individuality. These findings deepen our understanding of the biological foundations of inter-individual differences and how it evolves over the lifespan.

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bioRxiv2024

Model selection for spectral parameterization

Neurophysiological brain activity comprises rhythmic (periodic) and arrhythmic (aperiodic) signal elements, which are increasingly studied in relation to behavioral traits and clinical symptoms. Current methods for spectral parameterization of neural recordings rely on user-dependent parameter selection, which includes selecting the correct maximum number of spectral peaks to fit to the spectrum. This challenges the replicability and robustness of findings. Here, we introduce a data-driven model-selection procedure for determining the appropriate number of oscillatory peaks to fit to neural power spectra, based on the Bayesian Information Criterion (BIC). We present extensive tests of the approach with ground-truth and empirical magnetoencephalography recordings. Data-driven model selection enhances both the specificity and sensitivity of spectral decompositions. Overall, the proposed spectral decomposition with data-driven model selection reduces reliance on user-defined maximum number of peaks settings, enabling more robust, reproducible, and interpretable spectral parameterizations. Lay summaryBrain activity is composed of rhythmic patterns that repeat over time and arrhythmic elements that are less structured. Recent advances in brain signal analysis have improved our ability to distinguish between these two types of components, enhancing our understanding of brain signals. However, current methods require users to adjust several parameters manually to obtain their results. The outcomes of the analyses, therefore, depend on each users decisions and expertise. To improve the replicability of research findings, the authors propose a revised method to streamline the analysis of brain signal contents. They developed a new algorithm that defines the parameters of the analytical pipeline informed by the data. The effectiveness of this method is demonstrated with both synthesized and real-world data. The approach is made available to all researchers as a free, open-source app, observing best practices for neuroscience research.

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bioRxiv2024

Genetic Foundations of Neurophysiological and Behavioural Variability Across the Lifespan

Neurophysiological brain activity shapes cognitive functions and individual traits. Here, we investigated the extent to which individual neurophysiological properties are genetically determined and how these adult traits align with cortical gene expression patterns across development. Using task-free magnetoencephalography in monozygotic and dizygotic twins, as well as unrelated individuals, we found that neurophysiological traits were significantly more similar between monozygotic twins, indicating a genetic influence, although individual-specific variability remained predominant. These heritable brain dynamics were predominantly associated with genes involved in neurotransmission, expressed along a topographical gradient that mirrors psychological functions, including attention, planning, and emotional processes. Furthermore, the cortical expression patterns of genes associated with individual differentiation aligned most strongly with gene expression profiles observed during adulthood in previously published longitudinal datasets. These findings underscore a persistent genetic influence on neurophysiological activity, supporting individual cognitive and behavioral variability.

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bioRxiv2023

Unravelling the neural dynamics of hypnotic susceptibility: Aperiodic neural activity as a central feature of hypnosis

How well a person responds to hypnosis is a stable trait, which exhibits considerable inter-individual diversity across the general population. Yet, its neural underpinning remains elusive. Here, we address this gap by combining EEG data, multivariate statistics, and machine learning in order to identify brain patterns that differentiate between individuals high and low in susceptibility to hypnosis. In particular, we computed the periodic and aperiodic components of the EEG power spectrum, as well as graph theoretical measures derived from functional connectivity, from data acquired at rest (pre-induction) and under hypnosis (post-induction). We found that the 1/f slope of the EEG spectrum at rest was the best predictor of hypnotic susceptibility. Our findings support the idea that hypnotic susceptibility is a trait linked to the balance of cortical excitation and inhibition at baseline and offers novel perspectives on the neural foundations of hypnotic susceptibility. Future work can explore the contribution of background 1/f activity as a novel target to distinguish the responsiveness of individuals to hypnosis at baseline in the clinic. Significance StatementHypnotic phenomena reflect the ability to alter ones subjective experiences based on targeted verbal suggestions. This ability varies greatly in the population. The brain correlates to explain this variability remain elusive. Addressing this gap, our study employs machine learning to predict hypnotic susceptibility. By recording electroencephalography (EEG) before and after a hypnotic induction and analyzing diverse neurophysiological features, we were able to determine that several features differentiate between high and low hypnotic susceptible individuals both at baseline and during hypnosis. Our analysis revealed that the paramount discriminative feature is non-oscillatory EEG activity before the induction--a new finding in the field. This outcome aligns with the idea that hypnotic susceptibility represents a latent trait observable through a plain five-minutes resting-state EEG.

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bioRxiv2022

Differential and overlapping effects between exogenous and endogenous attention shape perceptual facilitation during visual processing

Visuospatial attention is not a monolithic process and can be divided into different functional systems. In this framework, exogenous attention reflects the involuntary orienting of attention resources following a salient event, whereas endogenous attention corresponds to voluntary orienting based on the goals and intentions of individuals. Previous work shows that these attention processes map onto distinct functional systems, yet evidence suggests that they are not fully independent. In the current work, we investigated the differential and overlapping effects of exogenous and endogenous attention on visual processing. We combined spatial cueing of visuospatial attention, electroencephalography (EEG), and multivariate pattern analysis (MVPA) to examine where and when the effects of exogenous and endogenous attention were maximally different and maximally similar. Critically, MVPA provided new insights by examining whether classifiers trained to decode the cueing effect for one attention process (e.g., exogenous attention) can successfully decode the cueing effect for the other attention process (e.g., endogenous attention). These analyses uncovered differential and overlapping effects between exogenous and endogenous attention. Next, we combined principal component analyses, single-trial event-related potentials, and mediation analysis to determine whether these effects facilitate perception, as indexed by the behavioral spatial cueing effects of exogenous and endogenous attention. This approach revealed that three EEG components shape the cueing effects of exogenous and endogenous attention at various times after target onset. Altogether, our study provides a comprehensive account about how overlapping and differential processes of endogenous and exogenous relate to perceptual facilitation in the context of visuospatial attention. Significance StatementTop-down and bottom-up attention represent separate functional systems in the brain. Previous research suggests, however, that they are not fully independent, and can interfere with each other. In the present study, the authors use machine learning techniques and recordings of brain activity to investigate differences and similarities between top-down and bottom-up attention during the visual processing of stimuli. This approach allowed them to explore how top-down and bottom-up attention processes facilitate perception. Their results show that top-down and bottom-up attention operate differently as early as 100ms after the onset of a target. In contrast, they operate similarly 200ms after the target onset. Most importantly, these effects are directly related to the participants perceptual behavior. In sum, our study shows that top-down and bottom-up attention support the perception of stimuli through overlapping and distinct spatio-temporal brain patterns.

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bioRxiv2022

Exogenous attention interferes with endogenous attention processing via lateralized alpha power

Current theories of attention differentiate exogenous (bottom-up) from endogenous (top-down) orienting of visuospatial attention. While both forms of attentional processing engage different processes, endogenous and exogenous attention are thought to share processing resources, as shown by recent empirical evidence of their functional interactions. Here, we aim to uncover the neurobiological basis of how salient events that drive exogenous attention disrupts endogenous attention processes. We hypothesize that interference from exogenous attention over endogenous attention involves alpha-band activity, a neural marker of visuospatial attention. To test this hypothesis, we contrast the effects of endogenous attention across two experimental conditions: a single cueing condition where endogenous attention is engaged in isolation, and a double cueing condition where endogenous attention is concurrently engaged with exogenous attention. Our results are consistent with previous work and show that the concurrent engagement of exogenous attention interferes with endogenous attention processes. Importantly, we evaluate our main hypothesis using a moderated mediation model. We found that changes in alpha-band activity mediate the relationship between endogenous attention and its benefits on task performance, and that the interference of exogenous attention on endogenous attention occurs via the moderation of this indirect effect. Altogether, our results substantiate a model of attention, whereby top-down and bottom-up attentional processes compete for shared neurophysiological resources. This model accounts for the observed patterns of interference between exogenous and endogenous attention. Significance StatementScientists differentiate top-down and bottom-up visuospatial attention processes. While bottom-up attention is rapidly engaged by emerging demands from the environment, top-down attention reflects slow voluntary shifts of attention. Several lines of research substantiate the idea that top-down and bottom-up attentional processes involve distinct brain systems. An increasing number of studies, however, argue that both attention systems share brain processing resources. The current study examines how salient visual events that engage bottom-up processes interfere with top-down processes. Using neurophysiological recordings and multivariate pattern classification techniques, the researchers show that both top-down and bottom-up attention processes do share brain resources expressed via alpha-band neurophysiological activity (8-12 Hz). The results further demonstrate that the interference patterns observed over brain activity in the alpha-band between both attention systems explains, in part, the interference between top-down and bottom-up attention at the behavioural level. The authors conclude by proposing a model of visuospatial attention whereby the dynamics between both attention systems are determined by their competition for limited brain processing resources.

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bioRxiv2022

Evidence for shared resources in visuospatial attention: Endogenous attention misleads the decoding of exogenous attention in alpha rhythms

Prevailing accounts of visuospatial attention differentiate exogenous (involuntary shifts) from endogenous (voluntary control) orienting of attention. While these two forms of attentional processes are functionally separable, their interactions have been at the center of ongoing debates for more than two decades. One hypothesis is that exogenous and endogenous attention interfere because they share processing resources. Here, we confirm that endogenous attention alters exogenous attention processing, and examine the role of alpha-band neurophysiological activity in such interference events. We contrast the effects of exogenous attention across two experimental conditions: a single-cueing condition where exogenous attention is engaged alone, and a double-cueing condition where exogenous attention is concurrently engaged with endogenous attention. Our results show that the engagement of endogenous attention alters the emergence of exogenous attention across cue-related and target-related brain processes. Importantly, we also report that classifiers trained to decode exogenous attention from the power and phase of alpha-band brain activity in the single-cueing condition fail to do so in the doublecueing condition, where endogenous attention is also engaged. Taken together, our observations challenge the idea that exogenous attention operates independently from top-down processes and demonstrate that both forms of attention orienting engage shared brain processes, which constrain their interactions. Significance StatementVisuospatial attention is often dichotomized into top-down and bottom-up components: Top-down attention reflects slow voluntary shifts of attention orienting, while bottom-up attention is recruited by emerging demands from the environment. A large body of previous findings support the view that these two forms of attention orienting are functionally separable, with some interactions. The current study examines such interactions between top-down and bottom-up attention. Using electroencephalography (EEG) and multivariate pattern classification techniques, the researchers show that top-down attention interferes with the brain activity patterns of bottom-up attention. Moreover, machine learning classifiers trained to detect bottom-up attention based on brain activity in the alpha band (8-12 Hz), a marker of visuospatial attention, fail systematically when top-down attention is also engaged. The authors therefore conclude that both forms of visuospatial orienting are supported by overlapping processes that share brain resources.

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bioRxiv2022

Time-resolved parameterization of aperiodic and periodic brain activity

Macroscopic neural dynamics comprise both aperiodic and periodic signal components. Recent advances in parameterizing neural power spectra offer practical tools for evaluating these features separately. Although neural signals vary dynamically and express non-stationarity in relation to ongoing behaviour and perception, current methods yield static spectral decompositions. Here, we introduce Spectral Parameterization Resolved in Time (SPRiNT) as a novel method for decomposing complex neural dynamics into periodic and aperiodic spectral elements in a time- resolved manner. First, we demonstrate with naturalistic synthetic data SPRiNTs capacity to reliably recover time-varying spectral features. We emphasize SPRiNTs specific strengths compared to other time-frequency parameterization approaches based on wavelets. Second, we use SPRiNT to illustrate how aperiodic spectral features fluctuate across time in empirical resting-state electroencephalography data (n = 178), and relate the observed changes in aperiodic parameters over time to participants demographics and behaviour. Lastly, we use SPRiNT to demonstrate how aperiodic dynamics relate to movement behaviour in intracranial recordings in rodents. We foresee SPRiNT responding to growing neuroscientific interests in the parameterization of time-varying neural power spectra and advancing the quantitation of complex neural dynamics at the natural time scales of behaviour. Significance StatementThe new method and reported findings address a growing interest in neuroscience for research tools that can reliably decompose brain activity at the mesoscopic scale into interpretable components. We show that the new approach proposed is capable of tracking transient, dynamic spectral (aperiodic and periodic) components across time, both in synthetic data and in in vivo experimental data. We anticipate that this novel technique, SPRiNT, will enable new neuroscience inquiries that reconcile multifaceted neural dynamics with complex behaviour.

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bioRxiv2021

Stability of spectral estimates in resting-state magnetoencephalography: recommendations for minimal data duration with neuroanatomical specificity

The principle of resting-state paradigms is appealing and practical for collecting data from impaired patients and special populations, especially if data collection times can be minimized. To achieve this goal, researchers need to ensure estimated signal features of interest are robust. In electro- and magnetoencephalography (EEG, MEG) we are not aware of studies of the minimal length of recording required to yield a robust one-session snapshot of the frequency-spectrum derivatives that are typically used to characterize the complex dynamics of the brains resting-state. We aimed to fill this knowledge gap by studying the stability of common spectral measures of resting-state MEG source time series obtained from large samples of single-session recordings from shared data repositories featuring different recording conditions and instrument technologies (OMEGA: N = 107; Cam-CAN: N = 50). We discovered that the rhythmic and arrhythmic spectral properties of intrinsic brain activity can be robustly estimated in most cortical regions when derived from relatively short recordings of 30-s to 120-s of resting-state data, regardless of instrument technology and resting-state paradigm. Using an adapted leave-one-out approach and Bayesian analysis, we also provide evidence that the stability of spectral features over time is unaffected by age, sex, handedness, and general cognitive function. In summary, short MEG sessions are sufficient to yield robust estimates of frequency-defined brain activity during resting-state. This study may help guide future empirical designs in the field, particularly when recording times need to be minimized, such as with patient or special populations.

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bioRxiv2021

MEG, myself, and I: individual identification from neurophysiological brain activity

Large, openly available datasets and current analytic tools promise the emergence of population neuroscience. The considerable diversity in personality traits and behaviour between individuals is reflected in the statistical variability of neural data collected in such repositories. This amount of variability challenges the sensitivity and specificity of analysis methods to capture the personal characteristics of a putative neural portrait. Recent studies with functional magnetic resonance imaging (fMRI) have concluded that patterns of resting-state functional connectivity can both successfully identify individuals within a cohort and predict some individual traits, yielding the notion of a neural fingerprint. Here, we aimed to clarify the neurophysiological foundations of individual differentiation from features of the rich and complex dynamics of resting-state brain activity using magnetoencephalography (MEG) in 158 participants. Akin to fMRI approaches, neurophysiological functional connectomes enabled the identification of individuals, with identifiability rates similar to fMRIs. We also show that individual identification was equally successful from simpler measures of the spatial distribution of neurophysiological spectral signal power. Our data further indicate that identifiability can be achieved from brain recordings as short as 30 seconds, and that it is robust over time: individuals remain identifiable from recordings performed weeks after their baseline reference data was collected. Based on these results, we can anticipate a vast range of further research and practical applications of individual differentiation from neural electrophysiology in personalized, clinical, and basic neuroscience.

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