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

Reproducibility and explainability in digital pathology: The need to make black-box artificial intelligence systems more transparent

Artificial intelligence (AI), and more specifically Machine Learning (ML) and Deep learning (DL), has permeated the digital pathology field in recent years, with many algorithms successfully applied as new advanced tools to analyze pathological tissues. The introduction of high-resolution scanners in histopathology services has represented a real revolution for pathologists, allowing the analysis of digital whole-slide images (WSI) on a screen without a microscope at hand. However, it means a transition from microscope to algorithms in the absence of specific training for most pathologists involved in clinical practice. The WSI approach represents a major transformation, even from a computational point of view. The multiple ML and DL tools specifically developed for WSI analysis may enhance the diagnostic process in many fields of human pathology. AI-driven models allow the achievement of more consistent results, providing valid support for detecting, from H&amp;E-stained sections, multiple biomarkers, including microsatellite instability, that are missed by expert pathologists

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

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

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 con-tributed 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 ar-bitrary) 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 re-porting findings based on any connectivity metric

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

Plaque-associated fibroblasts: Key regulators of atherosclerosis pathogenesis and plaque stability

Background and objective: Plaque-associated fibroblasts (PAFs) play a crucial role in shaping the plaque’s trajectory, either towards stability or instability. The pathological transformation of fibroblasts into myofibroblasts, characterized by increased contractility and secretion, contributes to excessive extracellular matrix (ECM) deposition. The bidirectional crosstalk between fibroblasts and inflammatory cells within the plaque is a crucial aspect. Activated fibroblasts release proinflammatory factors like interleukin-1 (IL-1), activating resident immune cells and facilitating their migration through the plaque microenvironment (PME). Conversely, immune cells produce cytokines such as IL-6, TNF-alpha, TGF-beta, and IL-1beta, stimulating fibroblasts to produce matrix metalloproteinase 1 (MMP1) and collagen deposition. The dynamic interplay among these cells, influenced by genetic predispositions, systemic conditions (hypertension, diabetes), inflammatory states (including COVID-19), and environmental factors (diet, lifestyle), determines the plaque’s fate. This review discusses the natural progression of carotid plaque and the evolving concepts surrounding the multiple events underlying vulnerable atherosclerotic lesions. Method: Google Scholar, Scopus, and PubMed were searched for manuscripts on PAFs and those reporting the association between PAFs and atherosclero-sis. Conclusion: Advances in our interpretation of histological images of atherosclerotic lesions may pave the way for novel therapeutic strategies aimed at inhibiting detrimental PAF activity, thereby facilitating further plaque stabilization and preventing severe clinical complications arising from carotid atherosclerotic plaque rupture. (www.actabiomedica.it)

Network Analysis of the HLS19-Q12 Health Literacy Questionnaire: insights from an Italian Pilot Study

Background. The widespread use of the internet and social media has transformed how people access health information impacting health literacy. Health literacy, the ability to access, understand, and use health information, is crucial to promote and maintain good health. This study is the first exploring with network analysis the correlation and distribution of the items of the Health Literacy Survey Questionnaire (HLS-Q) 12 short form to verify their correspondence to the principal domains of the health literacy conceptual model proposed by Sorensen et al. in 2013. Materials and Methods. A digital version of the Italian HLS19-Q12 questionnaire was distributed online through social media and informal channels in May 2024. The sample consisted of 352 participants from the metropolitan area of Cagliari, Italy. Network analysis was employed to examine the clustering and relationships between the questionnaire items, via JASP using the Ising Fit method. Results. Key findings include significant difficulties in accessing professional help and understanding medical emergencies. Network centrality measures highlighted the prominence of items related to understanding medical emergencies and making health decisions. Three clusters corresponding to healthcare, disease prevention, and health promotion, were visually identified with the last two closely interconnected. The item “making decisions to improve health” is crucial, acting as a bridge between clusters. Some items traditionally belonging to one domain shifted to another. Conclusions. The network analysis provided a clear depiction of health literacy as complex system, emphasizing interactions. Health literacy involves accessing, evaluating, and applying information, with empowerment playing a key role according to our findings. By addressing identified needs and focusing on prominent items, healthcare professionals and policymakers can enhance health literacy and improve health outcomes for individuals and communities. This pilot study’s findings could benefit future research and interventions to improve health literacy

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 &amp; 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 &lt; .001) and N3 (p &lt; .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