Abstract Episodic-memory impairment is a defining feature of Mild cognitive impairment (MCI), yet the large-scale neural processes through which medial temporal pathology translates into poor cognitive performance remain unclear. Fast brain activity can be organized into transient, aperiodic bursts—neuronal avalanches—that propagate from hippocampal and adjacent temporal regions across distributed brain networks, potentially indexing interactions relevant to memory. We therefore hypothesized that episodic-memory impairment in MCI reflects an altered ability of the temporal pole to initiate these activity cascades. We analyzed resting-state, source-reconstructed MEG recordings from 29 individuals with MCI and 32 healthy controls (HC). Large-scale dynamics were described in terms of neuronal avalanches, and we quantified each temporal-pole region’s propensity to act as an “avalanche starter” —that is, to be the first region to become active. We then related this measure to episodic-memory performance and hippocampal volume. Although temporal-pole starter frequency did not differ between groups, its relationship with memory performance was reversed. Greater avalanche initiation from the left temporal pole was associated with poorer memory performance in MCI, as measured by the delayed free recall in the Free and Cued Selective Reminding Test. The same relationship was positive in HC. Within the MCI group, greater left-temporal-pole starter frequency was also associated with hippocampal atrophy. These findings suggest that MCI involves a qualitative reorganization of temporal-pole–initiated dynamics rather than a simple change in their frequency. This framework links local structural vulnerability to altered whole-brain dynamics and episodic-memory impairment. Significance Statement Memory decline is a hallmark of Mild Cognitive Impairment (MCI), often a precursor to Alzheimer’s disease, yet how the brain’s large-scale dynamics change to produce this decline remains unclear. Using magnetoencephalography, we studied brief bursts of coordinated activity — neuronal avalanches — and introduced the “avalanche starter”: a region’s propensity to initiate, rather than join, one of these cascades. We focused on the temporal pole, among the earliest regions affected by Alzheimer’s pathology. In healthy adults, frequent avalanche initiation there was linked to better memory; in MCI, the same pattern was linked to worse memory and hippocampal atrophy. This reversal shows that early cognitive decline reflects a qualitative reorganization of temporal-pole-driven dynamics, rather than a simple change in activity levels.

Reduced graphene oxide (rGO) has attracted interest as a potential, cost-effective alternative to graphene layers produced by single-crystal thin-film growth techniques. Its solubility in various solvents, the ability to tune its optical and electrical properties, the ability to manipulate the optoelectronic properties of rGO-based heterojunctions, and the possibility of depositing it on flexible substrates broaden its potential applications, from electro-optical communications to environmental monitoring. In this work, we present a characterization of reduced graphene oxide (rGO) deposited on p-type Si3N4/Si substrate using different techniques such as Raman spectroscopy, optical transmittance, and current-voltage measurements under dark and illuminated conditions in the 400-700 nm range. Furthermore, the temperature dependence of the photocurrent of the rGO-based photoconductive device was studied in the temperature range from 300 K to 77 K. It has been shown that the electron transport mechanism through the p-type rGO/SiN/Si heterojunction at low voltage involves mainly a hopping process at 77 K and a thermionic mechanism at room temperature. Furthermore, the Fowler-Nordheim tunneling and trap-limiting mechanisms allow the presence of charge carriers in the device at both temperatures. Estimation of the main figures of merit, responsivity, detectivity, and NEP, shows an improvement in photodetection performance at low temperatures.

Abstract Amyotrophic Lateral Sclerosis (ALS) is increasingly recognized as a multisystem neurodegenerative disorder in which motor-neuron degeneration is accompanied by widespread alterations in cortical dynamics. Among its most reproducible neurophysiological signatures is cortical hyperexcitability, yet how this local excitability imbalance shapes distributed whole-brain activity remains poorly understood. Here, we combined source-reconstructed resting-state MEG data, tractography-informed whole-brain modeling, and simulation-based inference to investigate whether ALS-related alterations in large-scale brain dynamics can be mechanistically explained by changes in cortical excitability. First, we characterized empirical brain dynamics using complementary features spanning regional activity amplitude and variability, functional connectivity, and neuronal avalanche-based metrics. These analyses revealed significant alterations in ALS patients relative to healthy controls, as well as associations with clinical impairment and disease staging. To mechanistically interpret these changes, we employed a reduced Wong–Wang whole-brain model in which local recurrent excitation modulates emergent large-scale neural dynamics. Simulations showed that increasing excitability systematically reproduced the empirical dynamical signatures observed in ALS. We then applied a simulation-based inference framework to estimate latent excitability parameters directly from empirical observations. Whole-brain model inversion revealed increased excitability in ALS patients compared with controls. The recovered excitability parameter was associated with disease staging, supporting its clinical relevance as a model-derived descriptor of ALS progression. Finally, by extending the model to estimate frontal and non-frontal excitability separately, we found that ALS-related alterations were predominantly associated with increased frontal excitability, whereas non-frontal regions appeared comparatively less affected. The recovered parameters related to disease staging. Together, these findings provide a mechanistic framework linking altered large-scale brain dynamics in ALS to selective cortical hyperexcitability, explaining how local excitability changes can give rise to global network reorganization. More broadly, they show how computational model inversion can recover latent multiscale pathophysiological processes from empirical neural recordings, offering a non-perturbative alternative to complex experimental paradigms typically required to probe local-to-global mechanisms causally.

Abstract While behavioral fluctuations across the menstrual cycle (MC) are well-documented, the neural underpinnings of these changes remain elusive. This study investigated the hypothesis that cyclic variations in sex hormones modulate large-scale brain activation patterns. To test this, longitudinal magnetoencephalographic (MEG) recordings were acquired and source reconstructed from 24 naturally cycling women across three distinct MC phases: early follicular, peri-ovulatory, and mid-luteal. Microstate analysis was employed to characterize large-scale cortical dynamics as “visits” to specific global configurations (i.e., maps) of brain activity. Our results revealed significant variations in the occurrence of specific microstate maps, particularly between the early follicular and mid-luteal phases. Furthermore, the occurrence of these specific configurations was significantly associated with fluctuations in hormone levels. Critically, both the hormonal levels and microstate dynamics were predictive of individual longitudinal changes in psychological well-being. These findings propose a neurophysiological substrate for the behavioral effects of hormonal cycling, identifying specific topographic maps whose dynamics are sensitive to the hormonal profile and carry predictive power for psychological health. Collectively, these results underscore the necessity of accounting for the MC in neuroimaging research and introduce a novel framework for defining microstates (Hormone-Dependent Microstates - HDMs) with respect to slowly changing dynamical properties across a month-long timescale.

The electromagnetic response of metallic films is commonly analyzed in terahertz spectroscopy by assuming unit relative magnetic permeability. In this work we show that this assumption introduces significant distortions in the electrodynamic retrieval of highly conductive films. Aluminum and copper films, 10 nm thick, were investigated by terahertz time-domain spectroscopy in both transmission and reflection configurations. By applying a self consistent retrieval method that independently determines the complex permittivity and permeability, we show that the Drude-type dielectric response is systematically accompanied by a permeability that strongly departs from unity. This deviation is intrinsically linked to the reactive impedance of the films, which clarifies the light induced onset of large screening currents within a transversally confined geometry. A phenomenological interpretation based on the Faraday Neumann Lenz mechanism and a lumped-element model of the film impedance accounts for the observed trends. These results indicate that the common assumption =1 in non-magnetic Drude films can lead to an incomplete or biased electrodynamic characterization in the terahertz regime.

1 Abstract Brain activity can be understood as a sequence of neuronal avalanches, i.e., transient episodes of coordinated activation that emerge across scales, from individual neurons and local networks to whole-brain dynamics. Avalanches are typically characterized by features such as size, duration, number of active components, and the silent time separating consecutive events. Although these features have been extensively characterized through their marginal distributions, their temporal organization and dependence on the underlying brain architecture remain poorly understood, leaving us without a framework for embedding neuronal avalanches within slower brain dynamics. Here, we analyzed eyes-closed resting-state magnetoencephalography recordings and the corresponding structural connectomes from 30 healthy participants to investigate the dynamics of avalanche sizes and silent times. We found that large avalanches preferentially followed short silent times, whereas small avalanches were more likely to occur after long silent periods. Based on the empirical joint distributions of avalanche size and silent time, we could define four types of events occurring above chance levels (avalanche large or small, preceding pause long or short). Mixed categories—combining a small value of one feature with a large value of the other—occurred more frequently than expected, while same-category events happened less often than chance. Furthermore, consecutive events tended to remain in the same category, a phenomenon referred to as persistence. We next investigated whether a brain region’s connectivity profile shapes its propensity to participate in avalanches of different sizes. More strongly connected regions participated most often in small avalanches, whereas weakly connected regions were preferentially recruited during large avalanches. This pattern may reflect the greater sensitivity of highly connected hubs to fluctuations propagating through the network, resulting in frequent but spatially contained events. By contrast, the recruitment of more peripheral regions may require broader and stronger collective activity, occurring only during rarer, large-scale avalanches. In contrast, regional participation showed no clear association with the silent time preceding an avalanche. Together, these findings show that neuronal avalanches are neither temporally independent nor anatomically unconstrained: their sequence retains a memory of preceding events, while structural topology shapes which regions are recruited as avalanches grow. By connecting avalanche dynamics with slower temporal organization and the structural connectome, our results provide a multiscale framework for understanding how transient events are embedded within ongoing brain activity.

This review article aims to provide an overview of superconducting magnetic quantum sensors and their applications in the biomedical field, particularly in the neurological field. These quantum sensors are based on superconducting quantum interference devices (SQUIDs), the operating principles of which will be presented along with the most relevant characteristics. Emphasis will be placed on the magnetic flux and magnetic field noise, which are essential for applications, especially brain investigations requiring ultra-high magnetic field sensitivity. The main configurations of SQUID magnetometers used for highly sensitive applications will be shown, stressing their design aspects. In particular, the configurations based on the superconducting flux transformer and the multiloop will be explained. We will discuss the most critical application of SQUID magnetometers, magnetoencephalography, which measures the weak magnetic signals produced by neuronal currents. Starting from the realization of a multichannel system for magnetoencephalography, we will present an accurate comparison with recent systems using optically pumped magnetometers. Finally, we will discuss the main clinical applications of magnetoencephalography.

A healthy brain exhibits a rich dynamical repertoire, with flexible spatiotemporal patterns replaying on both microscopic and macroscopic scales. We hypothesize that the observed relationship between empirical structure and functional patterns is best explained when the microscopic neuronal dynamics is close to a critical regime. Using a modular spiking neuronal network model based on empirical connectomes, we posit that multiple stored functional patterns can transiently reoccur when the system operates near a critical regime, generating realistic brain dynamics and structural-functional relationships. The connections in the model are chosen so as to force the network to learn and propagate suited modular spatiotemporal patterns. To test our hypothesis, we employ magnetoencephalography and tractography data from five healthy individuals. We show that the extended critical region of the model maximizes the structure-function correlation and generates realistic features, demonstrating the relevance of near-critical regimes for physiological brain activity.

Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder that manifests through motor and non-motor symptoms. Understanding the alterations in brain connectivity associated with PD remains a challenge that is crucial for enhancing diagnosis and clinical management. Methods: This study utilized Magnetoencephalography (MEG) to investigate brain connectivity in PD patients compared to healthy controls (HCs) by applying eigenvector centrality (EC) measures across different frequency bands. Results: Our findings revealed significant differences in EC between PD patients and HCs in the alpha (8–12 Hz) and beta (13–30 Hz) frequency bands. To go into further detail, in the alpha frequency band, PD patients in the frontal lobe showed higher EC values compared to HCs. Additionally, we found statistically significant correlations between EC measures and clinical impairment scores (UPDRS-III). Conclusions: The proposed results suggest that MEG-derived EC measures can reveal important alterations in brain connectivity in PD, potentially serving as biomarkers for disease severity.

Multiple sclerosis (MS) is a clinically heterogeneous, multifactorial autoimmune disorder affecting the central nervous system. Structural damage to the myelin sheath, resulting in the consequent slowing of the conduction velocities, is a key pathophysiological mechanism. In fact, the conduction velocities are closely related to the degree of myelination, with thicker myelin sheaths associated to higher conduction velocities. However, how the intensity of the structural lesions of the myelin translates to slowing of nerve conduction delays is not known. In this work, we use large-scale brain models and Bayesian model inversion to estimate how myelin lesions translate to longer conduction delays across the damaged tracts. A cohort of 38 subjects (20 healthy and 18 with MS) underwent MEG recordings during an eyes-closed resting-state condition, along with MRI acquisitions and detailed white matter tractography analysis. We observed that MS patients consistently showed decreased power within the alpha frequency band (8-13 Hz) as compared to the healthy group. We also derived a lesion matrix indicating the percentage of lesions for each tract in every patient. Using large-scale brain modeling, the neural activity of each region was represented as a Stuart-Landau oscillator operating in a regime showing damped oscillations, and the regions were coupled according to subject-specific connectomes. We propose a linear formulation to the relationship between the conduction delays and the amount of structural damage in each white matter tract. Dependent upon the parameter γ $$ \upgamma $$ , this function translates lesions into edge-specific conduction delays (leading to shifts in the power spectra). Using deep neural density estimators, we found that the estimation of γ $$ \upgamma $$ showed a strong correlation with the alpha peak in MEG recordings. The most probable inferred γ $$ \upgamma $$ for each subject is inversely proportional to the observed peaks, while power peaks themselves do not correlate with total lesion volume. Furthermore, the estimated parameters were predictive (cross-sectionally) of individual clinical disability. This study represents the initial exploration showcasing the location-specific impact of myelin lesions on conduction delays, thereby enhancing the customization of models for individuals with multiple sclerosis.

Flux tuning of qubit frequencies in superconducting quantum processors is fundamental for implementing single and multi-qubit gates in quantum algorithms. Typical architectures involve the use of DC or fast RF lines. However, these lines introduce significant heat dissipation and undesirable decoherence mechanisms, leading to a severe bottleneck for scalability. Among different solutions to overcome this issue, we propose integrating tunnel Superconductor-Insulating-thin superconducting interlayer-Ferromagnet-Superconductor Josephson junctions (SIsFS JJs) into a novel transmon qubit design, the so-called ferrotransmon. SIsFS JJs provide memory properties due to the presence of ferromagnetic barriers and preserve at the same time the low-dissipative behavior of tunnel-insulating JJs, thus promoting an alternative tuning of the qubit frequency. In this work, we discuss the fundamental steps towards the implementation of this hybrid ferromagnetic transmon. We will give a special focus on the design, simulations, and preliminary experimental characterization of superconducting lines to provide in-plane magnetic fields, fundamental for an on-chip control of the qubit frequencies in the ferrotransmon.

By measuring the current-voltage characteristics and the switching current distributions as a function of temperature, we have investigated the phase dynamics of Al tunnel ferromagnetic Josephson junctions (JJs), designed to fall in the typical range of parameters of state-of-the-art transmons, providing evidence of phase diffusion processes. The comparison with the experimental outcomes on non-magnetic JJs with nominally the same electrodynamical parameters demonstrates that the introduction of ferromagnetic barriers does not cause any sizeable detrimental effect and supports the notion of including tunnel ferromagnetic JJs in qubit architectures.

Healthy brain exhibits a rich dynamical repertoire, with flexible spatiotemporal patterns replays on both microscopic and macroscopic scales. How do fixed structural connections yield a diverse range of dynamic patterns in spontaneous brain activity? We hypothesize that the observed relationship between empirical structure and functional patterns is best explained when the microscopic neuronal dynamics is close to a critical regime. Using a modular Spiking Neuronal Network model based on empirical connectomes, we posit that multiple stored functional patterns can transiently reoccur when the system operates near a critical regime, generating realistic brain dynamics and structural-functional relationships. The connections in the model are chosen as to force the network to learn and propagate suited modular spatiotemporal patterns. To test our hypothesis, we employ magnetoencephalography and tractography data from five healthy individuals. We show that the critical regime of the model is able to generate realistic features, and demonstrate the relevance of near-critical regimes for physiological brain activity. ### Competing Interest Statement The authors have declared no competing interest.

Magnetic Josephson junctions (MJJs) have emerged as a prominent playground to explore the interplay between superconductivity and ferromagnetism. A series of fascinating experiments have revealed striking phenomena at the superconductor/ferromagnet (S/F) interface, pointing to tunable phase transitions and to the generation of unconventional spin-triplet correlations. Here, we show that the Josephson effect, being sensitive to phase space variation on the nanoscale, allows a direct observation of the spin polarization of the S/F interface. By measuring the temperature dependence of the Josephson magnetic field patterns of tunnel MJJs with strong and thin F-layer, we demonstrate an induced nanoscale spin order in S along the superconducting coherence length at S/F interface, i.e., the inverse proximity effect, with the first evidence of full spin screening at very low temperatures, as expected by the theory. A comprehensive phase diagram for spin nanoscale ordering regimes at S/F interfaces in MJJs has been derived in terms of the magnetic moment induced in the S-layer. Our findings contribute to drive the design and the tailoring of S/F interfaces also in view of potential applications in quantum computing.

A superconducting quantum magnetometer for high-sensitivity applications has been developed by exploiting the flux focusing of the superconducting loop. Unlike conventional dc SQUID magnetometers that use a superconducting flux transformer or a multiloop design, in this case, a very simple design has been employed. It consists of a bare dc SQUID with a large washer-shaped superconducting ring in order to guarantee a magnetic field sensitivity BΦ less than one nT/Φ0. The degradation of the characteristics of the device due to an inevitable high value of the inductance parameter βL was successfully compensated by damping the inductance of the dc SQUID. The size of the magnetometer, coinciding with that of the washer, is 5 × 5 mm2 and the spectral density of the magnetic field noise is 8 fT/√Hz with a low frequency noise knee of two Hz. The excellent performance of this simple magnetometer makes it usable for all high-sensitivity applications including magnetoencephalography.

Nanomaterials have revolutionized the field of biosensors, offering unprecedented opportunities for enhanced sensitivity, selectivity, and miniaturization. Various nanomaterials, including metallic nanoparticles, carbon-based nano-materials, and nanocomposites, have been extensively explored for biosensing applications. Among these, MXene stands out as a particularly interesting two-dimensional (2D) nanomaterial, with numerous applications owing to its exceptional properties. The main objective of this study was to develop a novel laser scattering biosensor capable of detecting analytes through the aggregation of functionalized gold nanoparticle-decorated (Ti 3 C 2 )T x MXene (AuNPs@Ti 3 C 2 T x ) composites, leveraging the unique characteristics of both materials. Here, we integrated these biosensors with a fluidic system, achieving a rapid, sensitive, and cost-effective detection of various analytes, thereby facilitating high-throughput analysis, miniaturization, and automation.

This study examined the stability of the functional connectome (FC) over time using fingerprint analysis in healthy subjects. Additionally, it investigated how a specific stressor, namely sleep deprivation, affects individuals’ differentiation. To this aim, 23 healthy young adults underwent magnetoencephalography (MEG) recording at three equally spaced time points within 24 h: 9 a.m., 9 p.m., and 9 a.m. of the following day after a night of sleep deprivation. The findings indicate that the differentiation was stable from morning to evening in all frequency bands, except in the delta band. However, after a night of sleep deprivation, the stability of the FCs was reduced. Consistent with this observation, the reduced differentiation following sleep deprivation was found to be negatively correlated with the effort perceived by participants in completing the cognitive task during sleep deprivation. This correlation suggests that individuals with less stable connectomes following sleep deprivation experienced greater difficulty in performing cognitive tasks, reflecting increased effort.