Superconducting Quantum Magnetometer Based on Flux Focusing Effect for High-Sensitivity Applications.

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 ßL 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.

The Effect of Sleep Deprivation on Brain Fingerprint Stability: A Magnetoencephalography Validation Study.

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.

Brain fingerprint is based on the aperiodic, scale-free, neuronal activity.

Subject differentiation bears the possibility to individualize brain analyses. However, the nature of the processes generating subject-specific features remains unknown. Most of the current literature uses techniques that assume stationarity (e.g., Pearson's correlation), which might fail to capture the non-linear nature of brain activity. We hypothesize that non-linear perturbations (defined as neuronal avalanches in the context of critical dynamics) spread across the brain and carry subject-specific information, contributing the most to differentiability. To test this hypothesis, we compute the avalanche transition matrix (ATM) from source-reconstructed magnetoencephalographic data, as to characterize subject-specific fast dynamics. We perform differentiability analysis based on the ATMs, and compare the performance to that obtained using Pearson's correlation (which assumes stationarity). We demonstrate that selecting the moments and places where neuronal avalanches spread improves differentiation (P < 0.0001, permutation testing), despite the fact that most of the data (i.e., the linear part) are discarded. Our results show that the non-linear part of the brain signals carries most of the subject-specific information, thereby clarifying the nature of the processes that underlie individual differentiation. Borrowing from statistical mechanics, we provide a principled way to link emergent large-scale personalized activations to non-observable, microscopic processes.

Highly Sensitive Tunable Magnetometer Based on Superconducting Quantum Interference Device.

In the present article, experimental results regarding fully integrated superconducting quantum interference devices (SQUID), including a circuit to tune and optimize the main sensor device characteristics, are reported. We show the possibility of modifying the critical current of a SQUID magnetometer in liquid helium by means of a suitable heating circuit. This allows us to improve the characteristics of the SQUID sensor and in particular to optimize the voltage-magnetic flux characteristic and the relative transfer factor (responsivity) and consequently to also improve the flux and magnetic field noise. It is also possible to reset the SQUID sensor in case of entrapment of magnetic flux, avoiding taking it out of the helium bath. These results are very useful in view of most SQUID applications such as those requiring large multichannel systems in which it is desirable to optimize and eventually reset the magnetic sensors in a simple and effective way.

A night of sleep deprivation alters brain connectivity and affects specific executive functions.

Sleep is a fundamental physiological process necessary for efficient cognitive functioning especially in relation to memory consolidation and executive functions, such as attentional and switching abilities. The lack of sleep strongly alters the connectivity of some resting-state networks, such as default mode network and attentional network. In this study, by means of magnetoencephalography (MEG) and specific cognitive tasks, we investigated how brain topology and cognitive functioning are affected by 24 h of sleep deprivation (SD). Thirty-two young men underwent resting-state MEG recording and evaluated in letter cancellation task (LCT) and task switching (TS) before and after SD. Results showed a worsening in the accuracy and speed of execution in the LCT and a reduction of reaction times in the TS, evidencing thus a worsening of attentional but not of switching abilities. Moreover, we observed that 24 h of SD induced large-scale rearrangements in the functional network. These findings evidence that 24 h of SD is able to alter brain connectivity and selectively affects cognitive domains which are under the control of different brain networks.

Fine-Tuning and Optimization of Superconducting Quantum Magnetic Sensors by Thermal Annealing.

In the present article, we present the experimental results concerning the fine-tuning and optimization of superconducting quantum interference device (SQUID) parameters by thermal annealing. This treatment allows for the modification of the parameters in order to meet a specific application or to adjust the device parameters to prevent the increase of magnetic field noise and work instability conditions due to a different critical current with respect to the design value. In particular, we report the sensor critical current, the voltage-flux (V-Φ) characteristics and the spectral density of the magnetic field of SQUID magnetometers for different annealing temperatures. The measurements demonstrate that it is possible to achieve a fine control of the most important device parameters. In particular, we show that thermal annealing allows for the reduction of SQUID noise by more than a factor of 5 and makes the device working operations very stable. These results are very useful in view of quantum technology applications related to superconducting quantum computing where the correct functioning of the quantum bit depends on the fine control of the superconducting quantum device parameters and selectable annealing is possible by using a suitable laser as a thermal source.

Detection of Magnetomechanical Effect in Structural Steel Using GMR 2nd Order Gradiometer Based Sensors.

The magneto-mechanical behaviour of structural steel specimens stressed up to the plastic deformation stage was investigated using a 2nd order gradiometer based on Giant Magneto Resistive (GMR) sensors. The correlation between the gradient of the magnetization and the dislocation density before the crack initiation inside the test material was reported. The capability of the GMR scanning sensor to detect the residual magnetization due to the tensile stress with a non-invasive technique was demonstrated.

Multichannel system based on a high sensitivity superconductive sensor for magnetoencephalography.

We developed a multichannel system based on superconducting quantum interference devices (SQUIDs) for magnetoencephalography measurements. Our system consists of 163 fully-integrated SQUID magnetometers, 154 channels and 9 references, and all of the operations are performed inside a magnetically-shielded room. The system exhibits a magnetic field noise spectral density of approximatively 5 fT/Hz(1=2). The presented magnetoencephalography is the first system working in a clinical environment in Italy.

Surface sensitivity of Rayleigh anomalies in metallic nanogratings.

Sensing schemes based on Rayleigh anomalies (RAs) in metal nanogratings exhibit an impressive bulk refractive-index sensitivity determined solely by the grating period. However, the surface sensitivity (which is a key figure of merit for label-free chemical and biological sensing) needs to be carefully investigated to assess the actual applicability of this technological platform. In this paper, we explore the sensitivity of RAs in metal nanogratings when local refractive-index changes are considered. Our studies reveal that the surface sensitivity deteriorates up to two orders of magnitude by comparison with the corresponding bulk value; interestingly, this residual sensitivity is not attributable to the wavelength shift of the RAs, which are completely insensitive to local refractive-index changes, but rather to a strictly connected plasmonic effect. Our analysis for increasing overlay thickness reveals an ultimate surface sensitivity that approaches the RA bulk value, which turns out to be the upper-limit of grating-assisted surface-plasmon-polariton sensitivities.

Topological changes of fast large-scale brain dynamics in mild cognitive impairment predict early memory impairment: a resting-state, source reconstructed, magnetoencephalography study.

Functional connectivity has been used as a framework to investigate widespread brain interactions underlying cognitive deficits in mild cognitive impairment (MCI). However, many functional connectivity metrics focus on the average of the periodic activities, disregarding the aperiodic bursts of activity (i.e., the neuronal avalanches) characterizing the large-scale dynamic activities of the brain. Here, we apply the recently described avalanche transition matrix framework to source-reconstructed magnetoencephalography signals in a cohort of 32 MCI patients and 32 healthy controls to describe the spatio-temporal features of neuronal avalanches and explore their topological properties. Our results showed that MCI patients showed a more centralized network (as assessed by higher values of the degree divergence and leaf fraction) as compared to healthy controls. Furthermore, we found that the degree divergence (in the theta band) was predictive of hippocampal memory impairment. These findings highlight the role of the changes of aperiodic bursts in clinical conditions and may contribute to a more thorough phenotypical assessment of patients.

Brain Networks and Cognitive Impairment in Parkinson's Disease.

Aim: The aim of the present study is to investigate the relationship between both functional connectivity and brain networks with cognitive decline, in patients with Parkinson's disease (PD). Introduction: PD phenotype is not limited to motor impairment but, rather, a wide range of non-motor disturbances can occur, with cognitive impairment being one of the most common. However, how the large-scale organization of brain activity differs in cognitively impaired patients, as opposed to cognitively preserved ones, remains poorly understood. Methods: Starting from source-reconstructed resting-state magnetoencephalography data, we applied the phase linearity measurement (PLM) to estimate functional connectivity, globally and between brain areas, in PD patients with and without cognitive impairment (respectively PD-CI and PD-NC), as compared with healthy subjects (HS). Further, using graph analysis, we characterized the alterations in brain network topology and related these, as well as the functional connectivity, to cognitive performance. Results: We found reduced global and nodal PLM in several temporal (fusiform gyrus, Heschl's gyrus, and inferior temporal gyrus), parietal (postcentral gyrus), and occipital (lingual gyrus) areas within the left hemisphere, in the gamma band, in PD-CI patients, as compared with PD-NC and HS. With regard to the global topological features, PD-CI patients, as compared with HS and PD-NC patients, showed differences in multi-frequencies bands (delta, alpha, gamma) in the Leaf fraction, Tree hierarchy (Th) (both higher in PD-CI), and Diameter (lower in PD-CI). Finally, we found statistically significant correlations between the Montreal Cognitive Assessment test and both the Diameter in delta band and the Th in the alpha band. Conclusion: Our work points to specific large-scale rearrangements that occur selectively in cognitively compromised PD patients and are correlated to cognitive impairment. Impact statement In this article, we want to test the hypothesis that the cognitive decline observed in Parkinson's disease (PD) patients may be related to specific changes of both functional connectivity and brain network topology. Specifically, starting from magnetoencephalography signals and by applying the phase linearity measurement (PLM), a connectivity metric that measures the synchronization between brain regions, we were able to highlight differences in the global and nodal PLM values in PD patients with cognitive impairment as compared with both cognitively unimpaired patients and healthy subjects. Further, using graph analysis, we analyzed alterations in brain network topology that were related to cognitive functioning.

The progressive loss of brain network fingerprints in Amyotrophic Lateral Sclerosis predicts clinical impairment.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterised by functional connectivity alterations in both motor and extra-motor brain regions. Within the framework of network analysis, fingerprinting represents a reliable approach to assess subject-specific connectivity features within a given population (healthy or diseased). Here, we applied the Clinical Connectome Fingerprint (CCF) analysis to source-reconstructed magnetoencephalography (MEG) signals in a cohort of seventy-eight subjects: thirty-nine ALS patients and thirty-nine healthy controls. We set out to develop an identifiability matrix to assess the extent to which each patient was recognisable based on his/her connectome, as compared to healthy controls. The analysis was performed in the five canonical frequency bands. Then, we built a multilinear regression model to test the ability of the "clinical fingerprint" to predict the clinical evolution of the disease, as assessed by the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-r), the King's disease staging system, and the Milano-Torino Staging (MiToS) disease staging system. We found a drop in the identifiability of patients in the alpha band compared to the healthy controls. Furthermore, the "clinical fingerprint" was predictive of the ALSFRS-r (p = 0.0397; ß = 32.8), the King's (p = 0.0001; ß = -7.40), and the MiToS (p = 0.0025; ß = -4.9) scores. Accordingly, it negatively correlated with the King's (Spearman's rho = -0.6041, p = 0.0003) and MiToS scales (Spearman's rho = -0.4953, p = 0.0040). Our results demonstrated the ability of the CCF approach to predict the individual motor impairment in patients affected by ALS. Given the subject-specificity of our approach, we hope to further exploit it to improve disease management.

High-Quality Ferromagnetic Josephson Junctions Based on Aluminum Electrodes.

Aluminum Josephson junctions are the building blocks for the realization of superconducting quantum bits. Attention has been also paid to hybrid ferromagnetic Josephson junctions, which allow switching between different magnetic states, making them interesting for applications such as cryogenic memories, single-photon detectors, and spintronics. In this paper, we report on the fabrication and characterization of high-quality ferromagnetic Josephson junctions based on aluminum technology. We employed an innovative fabrication process inspired by niobium-based technology, allowing us to obtain very high-quality hybrid aluminum Josephson junctions; thus, supporting the use of ferromagnetic Josephson junctions in advanced quantum circuits. The fabrication process is described in detail and the main DC transport properties at low temperatures (current-voltage characteristic, critical current as a function of the temperature, and the external magnetic field) are reported. Here, we illustrate in detail the fabrication process, as well as the main DC transport properties at low temperatures (current-voltage characteristic, critical current as a function of the temperature, and the external magnetic field).

Core-Shell Magnetic Nanoparticles for Highly Sensitive Magnetoelastic Immunosensor.

A magnetoelastic (ME) biosensor for wireless detection of analytes in liquid is described. The ME biosensor was tested against human IgG in the range 0-20 µg∙mL-1. The sensing elements, anti-human IgG produced in goat, were immobilized on the surface of the sensor by using a recently introduced photochemical immobilization technique (PIT), whereas a new amplification protocol exploiting gold coated magnetic nanoparticles (core-shell nanoparticles) is demonstrated to significantly enhance the sensitivity. The gold nanoflowers grown on the magnetic core allowed us to tether anti-human IgG to the nanoparticles to exploit the sandwich detection scheme. The experimental results show that the 6 mm × 1 mm × 30 µm ME biosensor with an amplification protocol that uses magnetic nanoparticles has a limit of detection (LOD) lower than 1 nM, works well in water, and has a rapid response time of few minutes. Therefore, the ME biosensor is very promising for real-time wireless detection of pathogens in liquids and for real life diagnostic purpose.

Neuronal Avalanches to Study the Coordination of Large-Scale Brain Activity: Application to Rett Syndrome.

Many complex systems, such as the brain, display large-scale coordinated interactions that create ordered patterns. Classically, such patterns have been studied using the framework of criticality, i.e., at a transition point between two qualitatively distinct patterns. This kind of system is generally characterized by a scale-invariant organization, in space and time, optimally described by a power-law distribution whose slope is quantified by an exponent α. The dynamics of these systems is characterized by alternating periods of activations, called avalanches, with quiescent periods. To maximize its efficiency, the system must find a trade-off between its stability and ease of propagation of activation, which is achieved by a branching process. It is quantified by a branching parameter σ defined as the average ratio between the number of activations in consecutive time bins. The brain is itself a complex system and its activity can be described as a series of neuronal avalanches. It is known that critical aspects of brain dynamics are modeled with a branching parameter σ = , and the neuronal avalanches distribution fits well with a power law distribution exponent α = -3/2. The aim of our work was to study a self-organized criticality system in which there was a change in neuronal circuits due to genetic causes. To this end, we have compared the characteristics of neuronal avalanches in a group of 10 patients affected by Rett syndrome, during an open-eye resting-state condition estimated using magnetoencephalography, with respect to 10 healthy subjects. The analysis was performed both in broadband and in the five canonical frequency bands. We found, for both groups, a branching parameter close to 1. In this critical condition, Rett patients show a lower distribution parameter α in the delta and broadband. These results suggest that the large-scale coordination of activity occurs to a lesser extent in RTT patients.

Amnestic Mild Cognitive Impairment Is Associated With Frequency-Specific Brain Network Alterations in Temporal Poles.

There is general agreement that the neuropathological processes leading to Alzheimer's disease (AD) begin decades before the clinical onset. In order to detect early topological changes, we applied functional connectivity and network analysis to magnetoencephalographic (MEG) data obtained from 16 patients with amnestic Mild Cognitive Impairment (aMCI), a prodromal stage of AD, and 16 matched healthy control (HCs). Significant differences between the two groups were found in the theta band, which is associated with memory processes, in both temporal poles (TPs). In aMCI, the degree and betweenness centrality (BC) were lower in the left superior TP, whereas in the right middle TP the BC was higher. A statistically significant negative linear correlation was found between the BC of the left superior TP and a delayed recall score, a sensitive marker of the "hippocampal memory" deficit in early AD. Our results suggest that the TPs, which are involved early in AD pathology and belong to the memory circuitry, have an altered role in the functional network in aMCI.

Brain functional networks become more connected as amyotrophic lateral sclerosis progresses: a source level magnetoencephalographic study.

This study hypothesizes that the brain shows hyper connectedness as amyotrophic lateral sclerosis (ALS) progresses. 54 patients (classified as "early stage" or "advanced stage") and 25 controls underwent magnetoencephalography and MRI recordings. The activity of the brain areas was reconstructed, and the synchronization between them was estimated in the classical frequency bands using the phase lag index. Brain topological metrics such as the leaf fraction (number of nodes with degree of 1), the degree divergence (a measure of the scale-freeness) and the degree correlation (a measure of disassortativity) were estimated. Betweenness centrality was used to estimate the centrality of the brain areas. In all frequency bands, it was evident that, the more advanced the disease, the more connected, scale-free and disassortative the brain networks. No differences were evident in specific brain areas. Such modified brain topology is sub-optimal as compared to controls. Within this framework, our study shows that brain networks become more connected according to disease staging in ALS patients.