Lessons from autopsy: Topographical variability of atherosclerosis plaques.

Atherosclerosis is a complex disease characterized by the accumulation of plaques in arterial walls. Understanding its pathogenesis remains incomplete, with factors like inflammation, oxidative stress, and hypertension playing critical roles. The disease exhibits preferential localization of plaques, with variability observed even within the same individual. Genetic, environmental, and lifestyle factors contribute to its heterogeneity. Histological plaque phenotypes vary widely, prompting classification schemes focusing on systemic and local factors deteriorating fibrous caps. Recent research highlights differences in plaque histology among arterial systems, suggesting unique pathophysiological mechanisms. This study reports on multiple atherosclerotic plaques detected at autopsy in various vascular sites of a single subject, emphasizing their histological diversity and underscoring the systemic nature of atherosclerosis.

Wearable System Based on Ultra-Thin Parylene C Tattoo Electrodes for EEG Recording.

In an increasingly interconnected world, where electronic devices permeate every aspect of our lives, wearable systems aimed at monitoring physiological signals are rapidly taking over the sport and fitness domain, as well as biomedical fields such as rehabilitation and prosthetics. With the intent of providing a novel approach to the field, in this paper we discuss the development of a wearable system for the acquisition of EEG signals based on a portable, low-power custom PCB specifically designed to be used in combination with non-conventional ultra-conformable and imperceptible Parylene-C tattoo electrodes. The proposed system has been tested in a standard rest-state experiment, and its performance in terms of discrimination of two different states has been compared to that of a commercial wearable device for EEG signal acquisition (i.e., the Muse headset), showing comparable results. This first preliminary validation demonstrates the possibility of conveniently employing ultra-conformable tattoo-electrodes integrated portable systems for the unobtrusive acquisition of brain activity.

Clinical applications of EEG power spectra aperiodic component analysis: A mini-review.

OBJECTIVE: The present mini-review summarizes recent clinical findings related to the analysis of the aperiodic component of EEG (electroencephalographic) power spectra, making them quickly accessible to medical specialists and health researchers, with the aim of boosting related research. METHODS: Based on our experience about clinicians' literature-searching, we queried the PubMed database with terms related to EEG power spectra aperiodic component analysis and selected clinical studies that referenced such terms in the title/abstract, and were published in the last five years. RESULTS: A total of 11 journal articles, dealing with 9 different neurologic and psychiatric conditions published between 1st January 2016 - April 1st 2021, were surveyed. CONCLUSIONS: All the reviewed studies focused on exploring the pathophysiological significance of the aperiodic component and its correlation with disease presence, stage, and severity. Despite the heterogeneity of pathologies, it was possible to cluster most of them according to the mechanism underlying slope alterations, namely hypo-/hyper-excitability. It was also possible to identify some counterintuitive findings, probably related to compensation mechanisms of disease-specific neurophysiological alterations. SIGNIFICANCE: All the findings seem to support the role of the aperiodic activity as index of excitation/inhibition balance, with promising clinical applications that might challenge the traditional approach to pathologies diagnosis/treatment/follow-up.

Scorepochs: A Computer-Aided Scoring Tool for Resting-State M/EEG Epochs.

M/EEG resting-state analysis often requires the definition of the epoch length and the criteria in order to select which epochs to include in the subsequent steps. However, the effects of epoch selection remain scarcely investigated and the procedure used to (visually) inspect, label, and remove bad epochs is often not documented, thereby hindering the reproducibility of the reported results. In this study, we present Scorepochs, a simple and freely available tool for the automatic scoring of resting-state M/EEG epochs that aims to provide an objective method to aid M/EEG experts during the epoch selection procedure. We tested our approach on a freely available EEG dataset containing recordings from 109 subjects using the BCI2000 64 channel system.

Cortical and subcortical changes in resting-state neuronal activity and connectivity in early symptomatic ALS and advanced frontotemporal dementia.

The objective of this study was to examine if patterns of resting-state brain activity and functional connectivity in cortical and subcortical regions in patients with early symptomatic amyotrophic lateral sclerosis (ALS) resemble those of behavioural variant frontotemporal dementia (bvFTD). In a cross-sectional design, eyes-closed resting-state magnetoencephalography (MEG) data of 34 ALS patients, 18 bvFTD patients and 18 age- and gender-matched healthy controls (HCs) were projected to source-space using an atlas-based beamformer. Group differences in peak frequency, band-specific oscillatory activity and functional connectivity (corrected amplitude envelope correlation) in 78 cortical regions and 12 subcortical regions were determined. False discovery rate was used to correct for multiple comparisons. BvFTD patients, as compared to ALS and HCs, showed lower relative beta power in parietal, occipital, temporal and nearly all subcortical regions. Compared to HCs, patients with ALS and patients with bvFTD had a higher delta (0.5-4 Hz) and gamma (30-48 Hz) band resting-state functional connectivity in a high number of overlapping regions in the frontal lobe and in limbic and subcortical regions. Higher delta band connectivity was widespread in the bvFTD patients compared to HCs. ALS showed a more widespread higher gamma band functional connectivity compared to bvFTD. In conclusion, MEG in early symptomatic ALS patients shows resting-state functional connectivity changes in frontal, limbic and subcortical regions that overlap considerably with bvFTD. The findings show the potential of MEG to detect brain changes in early symptomatic phases of ALS and contribute to our understanding of the disease spectrum, with ALS and bvFTD at the two extreme ends.

Reshaping cortical connectivity in traumatic spinal cord injury: a novel effect of hyperbaric oxygen therapy.

INTRODUCTION: Spinal cord injuries (SCIs) represent a severe neuro-traumatic occurrence and an excruciating social burden. Though the hyperbaric oxygen (HBO2) has been credited as a first line therapeutic resource for SCIs, its mechanism of action in the spine is only partially known, while the impingement upon other areas of the nervous system deserves additional investigation. In this study we deem to describe a novel effect of HBO2 in a subject affected by SCI who, along with the clinical improvement, showed a reshaped connectivity in cortical sensory-motor areas. CASE PRESENTATION: A 45 years male presenting severe sensory-motor symptoms following a spinal lesion partially involving the C1 segment was successfully treated with HBO2 cycles. After the dramatic improvement reflected by an excellent optimization of the single performances, it has been investigated whether this result would reveal not only an intrinsic effect upon the spinal cord, but also a better connectivity strength in sensory-motor cortical regions. The results obtained by implementing EEG recordings with EEGLAB auto regressive vector plugins indeed suggest a substantial reshaping of cortico-cortical connectivity after HBO2. DISCUSSION: These results show a correlation between positive clinical evolution and a new modulation of cortical connectivity. Though further clinical investigations would clarify as to whether HBO2 might be directly or epiphenomenally involved in this aspect of the network architecture, our report suggests that a comparison between clinical results and the study of brain connectivity represent a holistic approach in investigating the physiopathology of SCIs and in monitoring the treatment.

Sleep-related hypermotor epilepsy and non-rapid eye movement parasomnias: Differences in the periodic and aperiodic component of the electroencephalographic power spectra.

Over the last two decades, our understanding of clinical and pathophysiological aspects of sleep-related epileptic and non-epileptic paroxysmal behaviours has improved considerably, although it is far from complete. Indeed, even if many core characteristics of sleep-related hypermotor epilepsy and non-rapid eye movement parasomnias have been clarified, some crucial points remain controversial, and the overlap of the behavioural patterns between these disorders represents a diagnostic challenge. In this work, we focused on segments of multichannel sleep electroencephalogram free from clinical episodes, from two groups of subjects affected by sleep-related hypermotor epilepsy (N = 15) and non-rapid eye movement parasomnias (N = 16), respectively. We examined sleep stages N2 and N3 of the first part of the night (cycles 1 and 2), and assessed the existence of differences in the periodic and aperiodic components of the electroencephalogram power spectra between the two groups, using the Fitting Oscillations & One Over f (FOOOF) toolbox. A significant difference in the gamma frequency band was found, with an increased relative power in sleep-related hypermotor epilepsy subjects, during both N2 (p < .001) and N3 (p < .001), and a significant higher slope of the aperiodic component in non-rapid eye movement parasomnias, compared with sleep-related hypermotor epilepsy, during N3 (p = .012). We suggest that the relative power of the gamma band and the slope extracted from the aperiodic component of the electroencephalogram signal may be helpful to characterize differences between subjects affected by non-rapid eye movement parasomnias and those affected by sleep-related hypermotor epilepsy.

On the Variability of Functional Connectivity and Network Measures in Source-Reconstructed EEG Time-Series.

The idea of estimating the statistical interdependence among (interacting) brain regions has motivated numerous researchers to investigate how the resulting connectivity patterns and networks may organize themselves under any conceivable scenario. Even though this idea has developed beyond its initial stages, its practical application is still far away from being widespread. One concurrent cause may be related to the proliferation of different approaches that aim to catch the underlying statistical interdependence among the (interacting) units. This issue has probably contributed to hindering comparisons among different studies. Not only do all these approaches go under the same name (functional connectivity), but they have often been tested and validated using different methods, therefore, making it difficult to understand to what extent they are similar or not. In this study, we aim to compare a set of different approaches commonly used to estimate the functional connectivity on a public EEG dataset representing a possible realistic scenario. As expected, our results show that source-level EEG connectivity estimates and the derived network measures, even though pointing to the same direction, may display substantial dependency on the (often arbitrary) choice of the selected connectivity metric and thresholding approach. In our opinion, the observed variability reflects the ambiguity and concern that should always be discussed when reporting findings based on any connectivity metric.

EEG Fingerprints under Naturalistic Viewing Using a Portable Device.

The electroencephalogram (EEG) has been proven to be a promising technique for personal identification and verification. Recently, the aperiodic component of the power spectrum was shown to outperform other commonly used EEG features. Beyond that, EEG characteristics may capture relevant features related to emotional states. In this work, we aim to understand if the aperiodic component of the power spectrum, as shown for resting-state experimental paradigms, is able to capture EEG-based subject-specific features in a naturalistic stimuli scenario. In order to answer this question, we performed an analysis using two freely available datasets containing EEG recordings from participants during viewing of film clips that aim to trigger different emotional states. Our study confirms that the aperiodic components of the power spectrum, as evaluated in terms of offset and exponent parameters, are able to detect subject-specific features extracted from the scalp EEG. In particular, our results show that the performance of the system was significantly higher for the film clip scenario if compared with resting-state, thus suggesting that under naturalistic stimuli it is even easier to identify a subject. As a consequence, we suggest a paradigm shift, from task-based or resting-state to naturalistic stimuli, when assessing the performance of EEG-based biometric systems.

EEG fingerprinting: Subject-specific signature based on the aperiodic component of power spectrum.

During the last few years, there has been growing interest in the effects induced by individual variability on activation patterns and brain connectivity. The practical implications of individual variability are of basic relevance for both group level and subject level studies. The Electroencephalogram (EEG), still represents one of the most used recording techniques to investigate a wide range of brain-related features. In this work, we aim to estimate the effect of individual variability on a set of very simple and easily interpretable features extracted from the EEG power spectra. In particular, in an identification scenario, we investigated how the aperiodic (1/f background) component of the EEG power spectra can accurately identify subjects from a large EEG dataset. The results of this study show that the aperiodic component of the EEG signal is characterized by strong subject-specific properties, that this feature is consistent across different experimental conditions (eyes-open and eyes-closed) and outperforms the canonically-defined frequency bands. These findings suggest that the simple features (slope and offset) extracted from the aperiodic component of the EEG signal are sensitive to individual traits and may help to characterize and make inferences at single subject-level.

Exploring the Correlation Between M/EEG Source-Space and fMRI Networks at Rest.

Magneto/electro-encephalography (M/EEG) source connectivity is an emerging approach to estimate brain networks with high temporal and spatial resolutions. Here, we aim to evaluate the effect of functional connectivity (FC) methods on the correlation between M/EEG source-space and fMRI networks at rest. Two main FC families are tested: (i) FC methods that do not remove zero-lag connectivity including Phase Locking Value (PLV) and Amplitude Envelope Correlation (AEC) and (ii) FC methods that remove zero-lag connections such as Phase Lag Index (PLI) and two orthogonalisation approaches combined with PLV (PLVCol, PLVPas) and AEC (AECCol, AECPas). Methods are evaluated on resting state M/EEG signals recorded from healthy participants at rest (N = 74). Networks obtained by each FC method are compared with fMRI networks (obtained from the Human Connectome Project). Results show low correlations for all FC methods, however PLV and AEC networks are significantly correlated with fMRI networks (ρ = 0.12, p = 1.93 × 10-8 and ρ = 0.06, p = 0.007, respectively), while other methods are not. These observations are consistent for all M/EEG frequency bands and for different FC matrices threshold. Our main message is to be careful in selecting FC methods when comparing or combining M/EEG with fMRI. We consider that more comparative studies based on simulation and real data and at different levels (node, module or sub networks) are still needed in order to improve our understanding on the relationships between M/EEG source-space networks and fMRI networks at rest.

Resting-state MEG measurement of functional activation as a biomarker for cognitive decline in MS.

BACKGROUND: Neurophysiological measures of brain function, such as magnetoencephalography (MEG), are widely used in clinical neurology and have strong relations with cognitive impairment and dementia but are still underdeveloped in multiple sclerosis (MS). OBJECTIVES: To demonstrate the value of clinically applicable MEG-measures in evaluating cognitive impairment in MS. METHODS: In eyes-closed resting-state, MEG data of 83 MS patients and 34 healthy controls (HCs) peak frequencies and relative power of six canonical frequency bands for 78 cortical and 10 deep gray matter (DGM) areas were calculated. Linear regression models, correcting for age, gender, and education, assessed the relation between cognitive performance and MEG biomarkers. RESULTS: Increased alpha1 and theta power was strongly associated with impaired cognition in patients, which differed between cognitively impaired (CI) patients and HCs in bilateral parietotemporal cortices. CI patients had a lower peak frequency than HCs. Oscillatory slowing was also widespread in the DGM, most pronounced in the thalamus. CONCLUSION: There is a clinically relevant slowing of neuronal activity in MS patients in parietotemporal cortical areas and the thalamus, strongly related to cognitive impairment. These measures hold promise for the application of resting-state MEG as a biomarker for cognitive disturbances in MS in a clinical setting.

A comparison between scalp- and source-reconstructed EEG networks.

EEG can be used to characterise functional networks using a variety of connectivity (FC) metrics. Unlike EEG source reconstruction, scalp analysis does not allow to make inferences about interacting regions, yet this latter approach has not been abandoned. Although the two approaches use different assumptions, conclusions drawn regarding the topology of the underlying networks should, ideally, not depend on the approach. The aim of the present work was to find an answer to the following questions: does scalp analysis provide a correct estimate of the network topology? how big are the distortions when using various pipelines in different experimental conditions? EEG recordings were analysed with amplitude- and phase-based metrics, founding a strong correlation for the global connectivity between scalp- and source-level. In contrast, network topology was only weakly correlated. The strongest correlations were obtained for MST leaf fraction, but only for FC metrics that limit the effects of volume conduction/signal leakage. These findings suggest that these effects alter the estimated EEG network organization, limiting the interpretation of results of scalp analysis. Finally, this study also suggests that the use of metrics that address the problem of zero lag correlations may give more reliable estimates of the underlying network topology.

EEG functional network topology is associated with disability in patients with amyotrophic lateral sclerosis.

Amyotrophic Lateral Sclerosis (ALS) is one of the most severe neurodegenerative diseases, which is known to affect upper and lower motor neurons. In contrast to the classical tenet that ALS represents the outcome of extensive and progressive impairment of a fixed set of motor connections, recent neuroimaging findings suggest that the disease spreads along vast non-motor connections. Here, we hypothesised that functional network topology is perturbed in ALS, and that this reorganization is associated with disability. We tested this hypothesis in 21 patients affected by ALS at several stages of impairment using resting-state electroencephalography (EEG) and compared the results to 16 age-matched healthy controls. We estimated functional connectivity using the Phase Lag Index (PLI), and characterized the network topology using the minimum spanning tree (MST). We found a significant difference between groups in terms of MST dissimilarity and MST leaf fraction in the beta band. Moreover, some MST parameters (leaf, hierarchy and kappa) significantly correlated with disability. These findings suggest that the topology of resting-state functional networks in ALS is affected by the disease in relation to disability. EEG network analysis may be of help in monitoring and evaluating the clinical status of ALS patients.

The effect of epoch length on estimated EEG functional connectivity and brain network organisation.

OBJECTIVE: Graph theory and network science tools have revealed fundamental mechanisms of functional brain organization in resting-state M/EEG analysis. Nevertheless, it is still not clearly understood how several methodological aspects may bias the topology of the reconstructed functional networks. In this context, the literature shows inconsistency in the chosen length of the selected epochs, impeding a meaningful comparison between results from different studies. APPROACH: The aim of this study was to provide a network approach insensitive to the effects that epoch length has on functional connectivity and network reconstruction. Two different measures, the phase lag index (PLI) and the amplitude envelope correlation (AEC) were applied to EEG resting-state recordings for a group of 18 healthy volunteers using non-overlapping epochs with variable length (1, 2, 4, 6, 8, 10, 12, 14 and 16 s). Weighted clustering coefficient (CCw), weighted characteristic path length (L w) and minimum spanning tree (MST) parameters were computed to evaluate the network topology. The analysis was performed on both scalp and source-space data. MAIN RESULTS: Results from scalp analysis show a decrease in both mean PLI and AEC values with an increase in epoch length, with a tendency to stabilize at a length of 12 s for PLI and 6 s for AEC. Moreover, CCw and L w show very similar behaviour, with metrics based on AEC more reliable in terms of stability. In general, MST parameters stabilize at short epoch lengths, particularly for MSTs based on PLI (1-6 s versus 4-8 s for AEC). At the source-level the results were even more reliable, with stability already at 1 s duration for PLI-based MSTs. SIGNIFICANCE: The present work suggests that both PLI and AEC depend on epoch length and that this has an impact on the reconstructed network topology, particularly at the scalp-level. Source-level MST topology is less sensitive to differences in epoch length, therefore enabling the comparison of brain network topology between different studies.

The re-organization of functional brain networks in pharmaco-resistant epileptic patients who respond to VNS.

Vagal nerve stimulation (VNS) is a therapeutic add-on treatment for patients with pharmaco-resistant epilepsy. The mechanism of action is still largely unknown. Previous studies have shown that brain network topology during the inter-ictal period in epileptic patients deviates from normal configuration. In the present paper, we investigate the relationship between clinical improvement induced by VNS and alterations in brain network topology. We hypothesize that, as a consequence of the VNS add-on treatment, functional brain network architecture shifts back toward a more efficient configuration in patients responding to VNS. Electroencephalographic (EEG) recordings from ten patients affected by pharmaco-resistant epilepsy were analyzed in the classical EEG frequency bands. The phase lag index (PLI) was used to estimate functional connectivity between EEG channels and the minimum spanning tree (MST) was computed in order to characterize VNS-induced alterations in network topology in a bias-free way. Our results revealed a clear network re-organization, in terms of MST modification, toward a more integrated architecture in patients responding to the VNS. In particular, the results show a significant interaction effect between benefit from VNS (responders/non-responders) and condition (pre/post VNS implantation) in the theta band. This finding suggests that the positive effect induced by VNS add-on treatment in epileptic patients is related to a clear network re-organization and that this network modification can reveal the long debated mechanism of action of VNS. Therefore, MST analysis could be useful in evaluating and monitoring the efficacy of VNS add-on treatment potentially in both epilepsy and psychiatric diseases.

Changes in MEG resting-state networks are related to cognitive decline in type 1 diabetes mellitus patients.

OBJECTIVE: Integrity of resting-state functional brain networks (RSNs) is important for proper cognitive functioning. In type 1 diabetes mellitus (T1DM) cognitive decrements are commonly observed, possibly due to alterations in RSNs, which may vary according to microvascular complication status. Thus, we tested the hypothesis that functional connectivity in RSNs differs according to clinical status and correlates with cognition in T1DM patients, using an unbiased approach with high spatio-temporal resolution functional network. METHODS: Resting-state magnetoencephalographic (MEG) data for T1DM patients with (n = 42) and without (n = 41) microvascular complications and 33 healthy participants were recorded. MEG time-series at source level were reconstructed using a recently developed atlas-based beamformer. Functional connectivity within classical frequency bands, estimated by the phase lag index (PLI), was calculated within eight commonly found RSNs. Neuropsychological tests were used to assess cognitive performance, and the relation with RSNs was evaluated. RESULTS: Significant differences in terms of RSN functional connectivity between the three groups were observed in the lower alpha band, in the default-mode (DMN), executive control (ECN) and sensorimotor (SMN) RSNs. T1DM patients with microvascular complications showed the weakest functional connectivity in these networks relative to the other groups. For DMN, functional connectivity was higher in patients without microangiopathy relative to controls (all p < 0.05). General cognitive performance for both patient groups was worse compared with healthy controls. Lower DMN alpha band functional connectivity correlated with poorer general cognitive ability in patients with microvascular complications. DISCUSSION: Altered RSN functional connectivity was found in T1DM patients depending on clinical status. Lower DMN functional connectivity was related to poorer cognitive functioning. These results indicate that functional connectivity may play a key role in T1DM-related cognitive dysfunction.

Brain network analysis of EEG functional connectivity during imagery hand movements.

The characterization of human neural activity during imaginary movement tasks represent an important challenge in order to develop effective applications that allow the control of a machine. Yet methods based on brain network analysis of functional connectivity have been scarcely investigated. As a result we use graph theoretic methods to investigate the functional connectivity and brain network measures in order to characterize imagery hand movements in a set of healthy subjects. The results of the present study show that functional connectivity analysis and minimum spanning tree (MST) parameters allow to successfully discriminate between imagery hand movements (both right and left) and resting state conditions. In conclusion, this paper shows that brain network analysis of EEG functional connectivity could represent an efficient alternative to more classical local activation based approaches. Furthermore, it also suggests the shift toward methods based on the characterization of a limited set of fundamental functional connections that disclose salient network topological features.

VNS induced desynchronization in gamma bands correlates with positive clinical outcome in temporal lobe pharmacoresistant epilepsy.

The vagus nerve stimulation (VNS) represents a diffuse non-pharmacological low-risk surgical option for epilepsy treatment. The aim of this study is to investigate the correlation between variations of global EEG synchronization and the clinical outcome in pharmacoresistant epileptic subjects implanted with VNS. Ten subjects affected by pharmacoresistant epilepsy were recruited on the basis of a clear-cut successful or unsuccessful outcome of the VNS add-on treatment. After five years from VNS surgery we examined the EEG in five subjects in each group. The investigation was led with the method of the phase lag index (PLI), which allows for the study of the global rate of synchronicity among the EEG signals before and after VNS implantation. The results of this study show that after five years from VNS surgery, in subjects whose seizures show a significant reduction, the desynchronization in the gamma frequency band is statistically decreased in comparison with patients who failed to show variations in the frequency and characteristics of their seizures. The other frequency bands are unaffected. This finding suggests that long lasting variations in gamma band desynchronization can be a new tool in assessing the efficacy of VNS. The possibility that GABA-mediated VNS-induced effects can also play a role in this result is discussed.

CINtec PLUS immunocytochemistry as a tool for the cytologic diagnosis of glandular lesions of the cervix uteri.

Cytologic findings of glandular lesions of the cervix uteri are often difficult to evaluate. We studied the usefulness of CINtec PLUS p16/Ki-67 double stain (mtm laboratories, Heidelberg, Germany) for the diagnosis of glandular lesions. The study included 47 abnormal results on liquid-based cytologic tests with a subsequent histologic diagnosis of adenocarcinoma in situ or with early invasion, and 16 samples with negative results on follow-up. All samples were stained with CINtec PLUS p16/Ki-67 double stain. Of the neoplastic samples, 7 were excluded because of insufficient residual cellularity or loss of neoplastic cells. Of the samples that were adequate, 92.5% were stained with CINtec PLUS, whereas 7.5% were judged inconclusive. All inconclusive cases were at least 3 years old. Of the 16 negative samples, 15 (93.8%) stained negative and only 1 (6.2%) showed several positive clusters of cells. Our study shows that CINtec PLUS is a robust and useful tool for the diagnosis of glandular lesions of the cervix uteri.