Biology subjects

Capilla, A.

Publications and source records attributed to Capilla, A..

9 recordsLinked to original sources

bioRxiv2026

Beyond power: A large-scale characterization of intrinsic brain oscillatory activity

Most of what we currently know about brain oscillations is derived from Fourier-based spectral power methods. While widely adopted, these procedures introduce methodological limitations and inherently overlook informative neurophysiological features that often remain unreported. In this study, we characterized resting-state oscillatory activity from human magnetoencephalography (MEG) recordings (N = 128) by integrating two complementary approaches. First, oscillatory episodes were detected at the source level with sBOSC. Subsequently, the ByCycle algorithm was applied to these episodes to extract individual cycle features. Results revealed that the brain engages in oscillatory activity for only [~]25% of the recording time, with occipital and parietal regions accounting for the highest temporal prevalence across canonical frequency bands. Furthermore, oscillatory episodes lasted an average of 4.6 cycles, reinforcing the view of neural oscillations as transient bursts. Region-specific duration and power measures revealed distinct anatomical organizations offering complementary physiological information. Finally, by extracting the instantaneous amplitude, period, and waveform asymmetry of individual cycles, we successfully dissociated sinusoidal occipital alpha waves from the asymmetric sensorimotor mu rhythm. By moving beyond traditional power-centric analyses, this approach provides a comprehensive characterization of spontaneous oscillatory activity, thereby offering new insights into the spatial, temporal, and spectral structure of human brain oscillations.

neuroscience↗

bioRxiv2026

Globally stable, locally flexible: Dynamic reconfiguration of brain natural frequencies during cognitive processing

Neural oscillations are fundamental to brain function and cognition. Conventional analyses often rely on predefined frequency bands to assess power modulations, which may obscure finer-grained spectral variability. In this study, we focused on frequency rather than power to investigate whether the natural frequency of each brain region, typically observed at rest, represents a stable intrinsic property or dynamically reconfigures during cognitive processing. We analysed magnetoencephalography (MEG) data from the Human Connectome Project (HCP) across motor execution, working memory, and language processing tasks. Using a multivariate, data-driven spectral clustering approach, we mapped natural frequencies on a voxel-by-voxel basis without imposing predefined bands or regional boundaries. Results indicated that, while the global spatial organization of natural frequencies remained largely preserved during task engagement, specific cortical regions exhibited systematic, task-dependent shifts. In the sensorimotor cortices, the typical resting frequency of [~]24 Hz decreased to [~]6 Hz during movement preparation and at movement onset, and shifted to high-beta rhythms ([~]30 Hz) following hand movement. Increased working memory demands accelerated parieto-occipital alpha/beta activity (from [~]11/16 Hz to [~]13/20 Hz) and recruited high-gamma oscillations (60 to 80 Hz) in medial temporal regions. Finally, arithmetic processing elicited a [~]5 to 15 Hz increase within the beta/gamma ranges across frontoparietal networks relative to semantic comprehension. Taken together, these findings demonstrate that natural frequencies reflect a hybrid architecture: globally stable, yet locally flexible in response to cognitive demands. Moreover, our results suggest that cognitive engagement tends to accelerate neural rhythms in functionally specialized regions, providing a more nuanced understanding of the spectral architecture of human brain function beyond conventional power- and band-based metrics.

neuroscience↗

bioRxiv2026

From MEG to low-density EEG: Reliable estimation of brain natural frequencies and their modulation across eyes-open and eyes-closed states

Neural oscillations are central to brain function and communication, yet they are typically characterized in terms of spectral power within predefined frequency bands, potentially obscuring their underlying functional organization. An alternative framework focuses on oscillatory frequency rather than power, revealing that each brain region exhibits a characteristic, or natural, frequency that can be estimated at the voxel level using a data-driven approach. Although this framework has been successfully applied to MEG, its broader use remains limited by cost and availability. Here, we extended this approach to EEG and validated it against MEG-derived maps, assessing its robustness across EEG channel densities (high-density, 64 channels; low-density, 32 channels) and physiological states (eyes open and closed). EEG-derived maps revealed a coherent spatial organization of natural frequencies across the cortex, reproducing the large-scale posterior-to-anterior and medial-to-lateral gradients of increasing frequency previously described with MEG. Differences between MEG and EEG were mainly confined to frontal and temporal regions, likely reflecting the differential sensitivity of the two techniques to neural source configurations, whereas posterior regions showed highly similar patterns. Importantly, this organization remained stable despite reductions in EEG sensor density and was modulated by physiological state, reproducing the well-known posterior alpha dominance during eyes-closed conditions. Together, these findings demonstrate that natural frequency mapping can be extended beyond specialized MEG research environments to low-density EEG settings, offering an accessible and scalable tool for investigating brain oscillations and their alterations in neuropsychiatric conditions.

neuroscience↗

bioRxiv2025

pBOSC: A method for source-level identification of neural oscillations in electromagnetic brain signals

Neural oscillations are recognized as a fundamental component of brain electromagnetic activity. They are implicated in a wide range of cognitive processes and proposed as a core mechanism for brain communication. Nonetheless, detecting genuine neural oscillations remains a methodological challenge, particularly due to the difficulty of distinguishing them from aperiodic background activity. To identify episodes of oscillatory activity directly at their sources, we developed sBOSC, which extends the BOSC (Better OSCillation detection) family of algorithms. Consistent with existing approaches, sBOSC detects oscillatory episodes that exceed both a defined power threshold and a minimum duration criterion. In sBOSC, however, the detection of oscillatory episodes also relies on identifying peaks (i.e., local maxima) in the power spectra as well as throughout the brain volume (spatial peaks). Using a series of simulated signals, we tested the ability of sBOSC to detect and localize oscillations across multiple scenarios. Our results show that most oscillatory episodes were accurately detected at their sources, achieving above 95% accuracy under optimal conditions (i.e., high signal-to-noise ratio, lower frequencies, and numerous successive cycles). In addition, we validated sBOSCs performance using real magnetoencephalography (MEG) data from both resting-state and motor task recordings. From the detected oscillatory episodes, we extracted a topographic distribution of natural frequencies that is consistent with previous work, as well as the expected alpha- and beta-band modulations over sensorimotor regions during motor preparation. In conclusion, sBOSC offers a novel approach for identifying oscillatory activity in electrophysiological signals. It extends previous algorithms by operating in source space and verifying the presence of genuine spectral peaks, thereby enabling new possibilities for exploring brain dynamics.

neuroscience↗

bioRxiv2025

Guess Who? Identifying individuals from their brain natural frequency fingerprints

Neural oscillations are critical for brain function and cognition. Thus, identifying the typical or natural oscillatory frequencies of the brain is an important first step for understanding its functional architecture. Recently, a data-driven algorithm has been developed for mapping the brains natural frequencies throughout the whole cortex, free of anatomical and frequency-band constraints. However, an important limitation of this methodology is that it yields robust results only at the group level. Here, we aimed to adapt this algorithm to improve the quality of the single-subject maps of natural frequencies obtained from magnetoencephalography (MEG) recordings. To achieve this goal, we incorporated two modifications to the original method: (1) increasing the number of individual power spectra to be assigned to each k-means cluster, and (2) smoothing across neighboring voxels. To assess the quality of the single-subject maps, we relied on the fingerprinting technique. Our results show a high degree of accuracy in individual identification, both within a single recording session and across separate sessions. Furthermore, we were able to identify individuals by their natural frequency fingerprints, even with a gap of over four years between sessions. This demonstrates the robustness of the single-subject mapping of natural frequencies and opens new opportunities for identification of pathological variations in intrinsic oscillatory activity in individual subjects.

neuroscience↗

bioRxiv2023

Perception of near-threshold visual stimuli is influenced by pre-stimulus alpha-band amplitude but not by alpha phase

Ongoing brain activity preceding visual stimulation has been suggested to shape conscious perception. The underlying mechanisms are still under debate, although alpha oscillations have been pointed out as the main explanatory candidate. According to the pulsed-inhibition framework, bouts of functional inhibition arise in each alpha cycle, allowing information to be processed in a pulsatile manner. Consequently, it has been hypothesized that perceptual outcome can be influenced by the specific phase of alpha oscillations prior to the stimulus onset, although empirical findings are controversial. In this study, we aimed to shed light on the role of pre-stimulus alpha oscillations in visual perception. To this end, we recorded electroencephalographic (EEG) activity while participants performed three near-threshold visual detection tasks with different attentional involvement: a no-cue task, a non-informative cue task (50% cue validity), and an informative cue task (100% cue validity). Cluster-based permutation statistics were complemented with Bayesian analyses to test the effect of pre-stimulus oscillatory amplitude and phase on visual awareness. We additionally examined whether these effects differed on trials with low and high oscillatory amplitude, as expected from the pulsed-inhibition theory. Our results show a clear effect of pre-stimulus alpha amplitude on conscious perception, but only when alpha fluctuated spontaneously and was not modulated by attention, supporting the notion that alpha-band power indexes neural excitability. In contrast, we did not find any evidence that pre-stimulus alpha phase influences the perceptual outcome, not even when differentiating between low and high amplitude trials. Furthermore, Bayesian analysis provided moderate evidence in favor of the absence of phase effects. Taken together, our results challenge the central theoretical predictions of the pulsed-inhibition framework, at least for the particular experimental conditions used here.

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bioRxiv2021

The spotlight of attention turns from rhythmic exploration-exploitation to a stable exploitation state

While traditional studies claim that visuospatial attention stays fixed at one location at a time, recent research has rather shown that attention rhythmically fluctuates between different locations at rates of prominent brain rhythms. However, little is known about the temporal dynamics of this fluctuation and, particularly, whether it changes over time. Thus, we addressed this question by investigating how visuospatial attention behaves over space and time. We recorded electroencephalographic activity of twenty-seven human participants while they performed a visuospatial cueing task, where attention was covertly oriented to the left or right visual field. In order to decode the spatial locus of attention from neural activity, we trained and tested a classifier on every timepoint of the orienting period, from the attentional cue to stimulus onset. This resulted in one temporal generalization matrix per participant, which was time-frequency decomposed to identify the sampling rhythm. Finally, a searchlight analysis was conducted to reveal the brain regions responsible for attention allocation. Our results show a dynamic evolution of the attentional spotlight, distinguishing between two states. In an early time window, attention explored both cued and uncued hemifield rhythmically at ~10 Hz. In a later time window attention focused on the cued hemifield. Classification was driven by occipital sources, while frontal regions exclusively became involved just before the spotlight settled onto the cued location. Together, our results define attentional sampling as a quasi-rhythmic dynamic process characterized by an initial rhythmic exploration-exploitation state, which is followed by a stable exploitation state.

neuroscience↗

bioRxiv2021

The natural frequencies of the resting human brain: an MEG-based atlas

Brain oscillations are considered to play a pivotal role in neural communication. However, detailed information regarding the typical oscillatory patterns of individual brain regions is surprisingly scarce. In this study we applied a multivariate data-driven approach to create an atlas of the natural frequencies of the resting human brain on a voxel-by-voxel basis. We analysed resting-state magnetoencephalography (MEG) data from 128 healthy adult volunteers obtained from the Open MEG Archive (OMEGA). Spectral power was computed in source space in 500 ms steps for 82 frequency bins logarithmically spaced from 1.7 to 99.5 Hz. We then applied k-means clustering to detect characteristic spectral profiles and to eventually identify the natural frequency of each voxel. Our results revealed a region-specific organisation of intrinsic oscillatory activity, following both a medial-to-lateral and a posterior-to-anterior gradient of increasing frequency. In particular, medial fronto-temporal regions were characterised by slow rhythms (delta/theta). Posterior regions presented natural frequencies in the alpha band, although with differentiated generators in the precuneus and in sensory-specific cortices (i.e., visual and auditory). Somatomotor regions were distinguished by the mu rhythm, while the lateral prefrontal cortex was characterised by oscillations in the high beta range (>20 Hz). Importantly, the brain map of natural frequencies was highly replicable in two independent subsamples of individuals. To the best of our knowledge, this is the most comprehensive atlas of ongoing oscillatory activity performed to date. Furthermore, the identification of natural frequencies is a fundamental step towards a better understanding of the functional architecture of the human brain.

neuroscience↗

bioRxiv2021

Suppression of alpha-band power underlies exogenous attention to emotional distractors

Alpha-band oscillations (8-14 Hz) are essential for attention and perception processes by facilitating the selection of relevant information. Directing visuospatial endogenous (voluntary) attention to a given location consistently results in a power suppression of alpha activity over occipito-parietal areas contralateral to the attended visual field. In contrast, the neural oscillatory dynamics underlying the involuntary capture of attention, or exogenous attention, are currently under debate. By exploiting the inherent capacity of emotionally salient visual stimuli to capture attention, we aimed to investigate whether exogenous attention is characterized by either a reduction or an increase in alpha-band activity. Electroencephalographic activity was recorded while participants completed a Posner visuospatial cueing task, in which a lateralized image with either positive, negative, or neutral emotional content competed with a target stimulus presented in the opposite hemifield. Compared with trials with no distractors, alpha power was reduced over occipital regions contralateral to distracting images. This reduction of alpha activity turned out to be functionally relevant, as it correlated with impaired behavioural performance on the ongoing task and was enhanced for distractors with negative valence. Taken together, our results demonstrate that visuospatial exogenous attention is characterized by a suppression of alpha-band activity contralateral to distractor location, similar to the oscillatory underpinnings of endogenous attention. Further, these results highlight the key role of exogenous attention as an adaptive mechanism for the efficient detection of biologically salient stimuli. HighlightsO_LIExogenous attention is indexed by alpha suppression contralateral to distractors. C_LIO_LIAlpha power decrease is enhanced by distractors with negative emotional valence. C_LIO_LILower levels of alpha power correlate with poorer task performance accuracy. C_LIO_LIThe negativity bias in exogenous attention might reflect an adaptive mechanism. C_LI

neuroscience↗