Biology subjects

Arana, L.

Publications and source records attributed to Arana, L..

7 recordsLinked to original sources

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

Identifying the internalization pathways of magnetotactic bacteria and magnetosomes by cancer cells

Given the need for new tumor treatment strategies, therapies using magnetic nanoparticles and bacteria are gaining momentum. In this context, magnetotactic bacteria and magnetosomes could act as theranostic agents for use in magnetic hyperthermia, targeted drug delivery, and magnetic resonance imaging. Thus, understanding their interaction with target cells is essential to ensure their theranostic efficiency. This study investigates the uptake of magnetotactic bacteria (MSR-1) and magnetosomes by lung carcinoma cells (A549). First, MSR-1 and magnetosomes are imaged inside A549 cells using cryo soft X-ray tomography, revealing the presence of MSR-1 and magnetosomes inside endosomes. Subsequently, the endocytosis pathways involved in the internalization of MSR-1 and magnetosomes by the cells are elucidated. It is observed that MSR-1 mainly enter cells by receptor-mediated endocytosis, as described previously for other intracellular bacteria. However, the endocytosis of magnetosomes occurs mainly via phagocytosis or macropinocytosis, probably due to the large size of the formed magnetosome clusters. These findings fill a key gap in our understanding of the internalization of MSR-1 bacteria and magnetosomes by lung carcinoma cells, and establish a method applicable to studying their internalization by other target cell types. Graphical AbstractMagnetotactic bacteria (Magnetospirillum gryphiswaldense MSR-1) and magnetosomes, which are proposed as cancer theranostic mediators, are imaged inside A549 cancer cells in 3D using cryo soft X-ray tomography. Moreover, the endocytosis pathways that the cells use to internalize MSR-1 and magnetosomes are elucidated. MSR-1 predominantly enter A549 cells via receptor-mediated endocytosis, while magnetosomes are preferentially internalized via receptor-independent endocytosis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/690460v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@bbcac0org.highwire.dtl.DTLVardef@fc33d6org.highwire.dtl.DTLVardef@1af65aeorg.highwire.dtl.DTLVardef@3ca539_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

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.

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↗