BACKGROUND: Modafinil is primarily used to treat narcolepsy but is also used as an off-label cognitive enhancer. Functional magnetic resonance imaging studies indicate that modafinil modulates the connectivity of neocortical networks primarily involved in attention and executive functions. However, much less is known about the drug's effects on subcortical structures. Following preliminary findings, we evaluated modafinil's activity on the connectivity of distinct cerebellar regions with the neocortex. We assessed the spatial relationship of these effects with the expression of neurotransmitter receptors/transporters. METHODS: Patterns of resting-state functional magnetic resonance imaging connectivity were estimated in 50 participants from scans acquired pre- and postadministration of a single (100 mg) dose of modafinil (n = 25) or placebo (n = 25). Using specific cerebellar regions as seeds for voxelwise analyses, we examined modafinil's modulation of cerebellar-neocortical connectivity. Next, we conducted a quantitative evaluation of the spatial overlap between the modulation of cerebellar-neocortical connectivity and the expression of neurotransmitter receptors/transporters obtained by publicly available databases. RESULTS: Modafinil increased the connectivity of crus I and vermis IX with prefrontal regions. Crus I connectivity changes were associated with the expression of dopaminergic D2 receptors. The vermis I-II showed enhanced coupling with the dorsal anterior cingulate cortex and matched the expression of histaminergic H3 receptors. The vermis VII-VIII displayed increased connectivity with the visual cortex, an activity associated with dopaminergic and histaminergic neurotransmission. CONCLUSIONS: Our study reveals modafinil's modulatory effects on cerebellar-neocortical connectivity. The modulation mainly involves crus I and the vermis and spatially overlaps the distribution of dopaminergic and histaminergic receptors.

• MeSH
• Adult MeSH
• Humans MeSH
• Magnetic Resonance Imaging * MeSH
• Young Adult MeSH
• Modafinil * pharmacology   administration & dosage   MeSH
• Cerebellum * drug effects   diagnostic imaging   metabolism   MeSH
• Neocortex drug effects   metabolism   diagnostic imaging   MeSH
• Neural Pathways drug effects   metabolism   MeSH
• Wakefulness-Promoting Agents pharmacology   MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Young Adult MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Randomized Controlled Trial MeSH

This study explored how the human cortical folding pattern composed of convex gyri and concave sulci affected single-subject morphological brain networks, which are becoming an important method for studying the human brain connectome. We found that gyri-gyri networks exhibited higher morphological similarity, lower small-world parameters, and lower long-term test-retest reliability than sulci-sulci networks for cortical thickness- and gyrification index-based networks, while opposite patterns were observed for fractal dimension-based networks. Further behavioral association analysis revealed that gyri-gyri networks and connections between gyral and sulcal regions significantly explained inter-individual variance in Cognition and Motor domains for fractal dimension- and sulcal depth-based networks. Finally, the clinical application showed that only sulci-sulci networks exhibited morphological similarity reductions in major depressive disorder for cortical thickness-, fractal dimension-, and gyrification index-based networks. Taken together, these findings provide novel insights into the constraint of the cortical folding pattern to the network organization of the human brain.

• MeSH
• Depressive Disorder, Major pathology   diagnostic imaging   MeSH
• Adult MeSH
• Connectome * MeSH
• Humans MeSH
• Magnetic Resonance Imaging * MeSH
• Young Adult MeSH
• Cerebral Cortex * diagnostic imaging   anatomy & histology   MeSH
• Nerve Net * diagnostic imaging   anatomy & histology   MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Young Adult MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

Current diagnostic methods for dyslexia primarily rely on traditional paper-and-pencil tasks. Advanced technological approaches, including eye-tracking and artificial intelligence (AI), offer enhanced diagnostic capabilities. In this paper, we bridge the gap between scientific and diagnostic concepts by proposing a novel dyslexia detection method, called INSIGHT, which combines a visualisation phase and a neural network-based classification phase. The first phase involves transforming eye-tracking fixation data into 2D visualisations called Fix-images, which clearly depict reading difficulties. The second phase utilises the ResNet18 convolutional neural network for classifying these images. The INSIGHT method was tested on 35 child participants (13 dyslexic and 22 control readers) using three text-reading tasks, achieving a highest accuracy of 86.65%. Additionally, we cross-tested the method on an independent dataset of Danish readers, confirming the robustness and generalizability of our approach with a notable accuracy of 86.11%. This innovative approach not only provides detailed insight into eye movement patterns when reading but also offers a robust framework for the early and accurate diagnosis of dyslexia, supporting the potential for more personalised and effective interventions.

• MeSH
• Reading MeSH
• Child MeSH
• Dyslexia * physiopathology   diagnosis   classification   MeSH
• Humans MeSH
• Neural Networks, Computer * MeSH
• Fixation, Ocular * physiology   MeSH
• Eye Movements physiology   MeSH
• Eye-Tracking Technology * MeSH
• Check Tag
• Child MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

UNLABELLED: Schizophrenia is a complex disorder characterized by altered brain functional connectivity, detectable during both task and resting state conditions using different neuroimaging methods. To this day, electroencephalography (EEG) studies have reported inconsistent results, showing both hyper- and hypo-connectivity with diverse topographical distributions. Interpretation of these findings is complicated by volume-conduction effects, where local brain activity fluctuations project simultaneously to distant scalp regions (zero-phase lag), inducing spurious inter-electrode correlations. AIM: In the present study, we explored the network dynamics of schizophrenia using a novel functional connectivity metric-corrected imaginary phase locking value (ciPLV)-which is insensitive to changes in amplitude as well as interactions at zero-phase lag. This method, which is less prone to volume conduction effects, provides a more reliable estimate of sensor-space functional network connectivity in schizophrenia. METHODS: We employed a cross-sectional design, utilizing resting state EEG recordings from two adult groups: individuals diagnosed with chronic schizophrenia (n = 30) and a control group of healthy participants (n = 30), all aged between 18 and 55 years old. RESULTS: Our observations revealed that schizophrenia is characterized by a prevalence of excess theta (4-8 Hz) power localized to centroparietal electrodes. This was accompanied by significant alterations in inter- and intra-hemispheric functional network connectivity patterns, mainly between frontotemporal regions within the theta band and frontoparietal regions within beta/gamma bands. CONCLUSIONS: Our findings suggest that patients with schizophrenia demonstrate long-range electrophysiological connectivity abnormalities that are independent of spectral power (i.e., volume conduction). Overall, distinct hemispheric differences were present in frontotemporo-parietal networks in theta and beta/gamma bands. While preliminary, these alterations could be promising new candidate biomarkers of chronic schizophrenia.

• MeSH
• Chronic Disease MeSH
• Adult MeSH
• Electroencephalography * methods   MeSH
• Middle Aged MeSH
• Humans MeSH
• Adolescent MeSH
• Young Adult MeSH
• Brain physiopathology   diagnostic imaging   MeSH
• Nerve Net physiopathology   diagnostic imaging   MeSH
• Rest physiology   MeSH
• Cross-Sectional Studies MeSH
• Schizophrenia * physiopathology   diagnostic imaging   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Adolescent MeSH
• Young Adult MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

Digitalizace postupně proniká do velké části medicínských oblastí včetně patologie. Společně s digitálním zpracováním dat přichází aplikace metod umělé inteligence za účelem zjednodušení rutinních procesů, zvýšení bezpečnosti apod. Ačkoliv se obecné povědomí o metodách umělé inteligence zvyšuje, stále není pravidlem, že by odborníci z netechnických oborů měli detailní představu o tom, jak takové systémy fungují a jak se učí. Cílem tohoto textu je přístupnou formou vysvětlit základy strojového učení s využitím příkladů a ilustrací z oblasti digitální patologie. Nejedná se samozřejmě o ucelený přehled ani o představení nejmodernějších metod. Držíme se spíše úplných základů a představujeme fundamentální myšlenky, které stojí za většinou učících systémů, s použitím nejjednodušších modelů. V textu se věnujeme zejména rozhodovacím stromům, jejichž funkce je snadno vysvětlitelná, a elementárním neuronovým sítím, které jsou hlavním modelem používaným v dnešní umělé inteligenci. Pokusíme se také popsat postup spolupráce mezi lékaři, kteří dodávají data, a informatiky, kteří s jejich pomocí vytvářejí učící systémy. Věříme, že tento text pomůže překlenout rozdíly mezi znalostmi lékařů a informatiků a tím přispěje k efektivnější mezioborové spolupráci.

Digitalization has gradually made its way into many areas of medicine, including pathology. Along with digital data processing comes the application of artificial intelligence methods to simplify routine processes, enhance safety, etc. Although general awareness of artificial intelligence methods is increasing, it is still not common for professionals from non-technical fields to have a detailed understanding of how such systems work and learn. This text aims to explain the basics of machine learning in an accessible way using examples and illustrations from digital pathology. This is not intended to be a comprehensive overview or an introduction to cutting-edge methods. Instead, we use the simplest models to focus on fundamental concepts behind most learning systems. The text concentrates on decision trees, whose functionality is easy to explain, and basic neural networks, the primary models used in today’s artificial intelligence. We also attempt to describe the collaborative process between medical specialists, who provide the data, and computer scientists, who use this data to develop learning systems. This text will help bridge the knowledge gap between medical professionals and computer scientists, contributing to more effective interdisciplinary collaboration.

• MeSH
• Humans MeSH
• Pathology * trends   MeSH
• Machine Learning * trends   MeSH
• Artificial Intelligence trends   MeSH
• Check Tag
• Humans MeSH

This study aimed to directly compare electroencephalography (EEG) whole-brain patterns of neural dynamics with concurrently measured fMRI BOLD data. To achieve this, we aim to derive EEG patterns based on a spatio-spectral decomposition of band-limited EEG power in the source-reconstructed space. In a large dataset of 72 subjects undergoing resting-state hdEEG-fMRI, we demonstrated that the proposed approach is reliable in terms of both the extracted patterns as well as their spatial BOLD signatures. The five most robust EEG spatio-spectral patterns not only include the well-known occipital alpha power dynamics, ensuring consistency with established findings, but also reveal additional patterns, uncovering new insights into brain activity. We report and interpret the most reproducible source-space EEG-fMRI patterns, along with the corresponding EEG electrode-space patterns, which are better known from the literature. The EEG spatio-spectral patterns show weak, yet statistically significant spatial similarity to their functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent (BOLD) signatures, particularly in the patterns that exhibit stronger temporal synchronization with BOLD. However, we did not observe a statistically significant relationship between the EEG spatio-spectral patterns and the classical fMRI BOLD resting-state networks (as identified through independent component analysis), tested as the similarity between their temporal synchronization and spatial overlap. This provides evidence that both EEG (frequency-specific) power and the BOLD signal capture reproducible spatio-temporal patterns of neural dynamics. Instead of being mutually redundant, these only partially overlap, providing largely complementary information regarding the underlying low-frequency dynamics.

• Publication type
• Journal Article MeSH

BACKGROUND: Advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized the medical field and transformed translational medicine. These technologies enable more accurate disease trajectory models while enhancing patient-centered care. However, challenges such as heterogeneous datasets, class imbalance, and scalability remain barriers to achieving optimal predictive performance. METHODS: This study proposes a novel AI-based framework that integrates Gradient Boosting Machines (GBM) and Deep Neural Networks (DNN) to address these challenges. The framework was evaluated using two distinct datasets: MIMIC-IV, a critical care database containing clinical data of critically ill patients, and the UK Biobank, which comprises genetic, clinical, and lifestyle data from 500,000 participants. Key performance metrics, including Accuracy, Precision, Recall, F1-Score, and AUROC, were used to assess the framework against traditional and advanced ML models. RESULTS: The proposed framework demonstrated superior performance compared to classical models such as Logistic Regression, Random Forest, Support Vector Machines (SVM), and Neural Networks. For example, on the UK Biobank dataset, the model achieved an AUROC of 0.96, significantly outperforming Neural Networks (0.92). The framework was also efficient, requiring only 32.4 s for training on MIMIC-IV, with low prediction latency, making it suitable for real-time applications. CONCLUSIONS: The proposed AI-based framework effectively addresses critical challenges in translational medicine, offering superior predictive accuracy and efficiency. Its robust performance across diverse datasets highlights its potential for integration into real-time clinical decision support systems, facilitating personalized medicine and improving patient outcomes. Future research will focus on enhancing scalability and interpretability for broader clinical applications.

• MeSH
• Databases, Factual MeSH
• Humans MeSH
• Neural Networks, Computer MeSH
• Patient-Centered Care * MeSH
• Machine Learning * MeSH
• Translational Science, Biomedical MeSH
• Translational Research, Biomedical MeSH
• Artificial Intelligence * MeSH
• Treatment Outcome MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

Electroencephalography (EEG) experiments typically generate vast amounts of data due to the high sampling rates and the use of multiple electrodes to capture brain activity. Consequently, storing and transmitting these large datasets is challenging, necessitating the creation of specialized compression techniques tailored to this data type. This study proposes one such method, which at its core uses an artificial neural network (specifically a convolutional autoencoder) to learn the latent representations of modelled EEG signals to perform lossy compression, which gets further improved with lossless corrections based on the user-defined threshold for the maximum tolerable amplitude loss, resulting in a flexible near-lossless compression scheme. To test the viability of our approach, a case study was performed on the 256-channel binocular rivalry dataset, which also describes mostly data-specific statistical analyses and preprocessing steps. Compression results, evaluation metrics, and comparisons with baseline general compression methods suggest that the proposed method can achieve substantial compression results and speed, making it one of the potential research topics for follow-up studies.

• MeSH
• Adult MeSH
• Electroencephalography * methods   MeSH
• Data Compression * methods   MeSH
• Humans MeSH
• Neural Networks, Computer * MeSH
• Signal Processing, Computer-Assisted MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

Space and time are fundamental attributes of the external world. Deciphering the brain mechanisms involved in processing the surrounding environment is one of the main challenges in neuroscience. This is particularly defiant when situations change rapidly over time because of the intertwining of spatial and temporal information. However, understanding the cognitive processes that allow coping with dynamic environments is critical, as the nervous system evolved in them due to the pressure for survival. Recent experiments have revealed a new cognitive mechanism called time compaction. According to it, a dynamic situation is represented internally by a static map of the future interactions between the perceived elements (including the subject itself). The salience of predicted interactions (e.g. collisions) over other spatiotemporal and dynamic attributes during the processing of time-changing situations has been shown in humans, rats, and bats. Motivated by this ubiquity, we study an artificial neural network to explore its minimal conditions necessary to represent a dynamic stimulus through the future interactions present in it. We show that, under general and simple conditions, the neural activity linked to the predicted interactions emerges to encode the perceived dynamic stimulus. Our results show that this encoding improves learning, memorization and decision making when dealing with stimuli with impending interactions compared to no-interaction stimuli. These findings are in agreement with theoretical and experimental results that have supported time compaction as a novel and ubiquitous cognitive process.

• MeSH
• Humans MeSH
• Brain physiology   MeSH
• Neural Networks, Computer * MeSH
• Memory physiology   MeSH
• Decision Making physiology   MeSH
• Learning physiology   MeSH
• Time Perception physiology   MeSH
• Space Perception physiology   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

Diet, stress, genetics, and a sedentary lifestyle may all contribute to heart disease rates. Although recent studies propose comprehensive automated diagnostic systems, these systems tend to focus on one aspect, such as feature selection, prioritization, or predictive accuracy. A more complete approach that considers all of these factors can improve the efficiency of a cardiac prediction system. This study uses an appropriate strategy to overcome potential network design problems, design challenges, overfitting, and lack of robustness that can interfere with system performance. The research introduces an ideally designed deep trust network called ID-DTN to improve system performance. The Ruzzo-Tompa method is used to eliminate noncontributory features. The Seagull Optimization Algorithm (SOA) is introduced to optimize the trust depth network to achieve optimal network design. The study scrutinizes the deep trust network (ID-DTN) and the restricted Boltzmann machine (RBM) and sheds light on the system's operation. This proposal can optimize both network architecture and feature selection, which is the main novelty. The proposed method is analyzed using the below-mentioned metrics: Matthew's correlation coefficient, F1 score, accuracy, sensitivity, specificity, and accuracy. ID-DTN performs well compared to other state-of-the-art methods. The validation results confirm that the proposed method improves the prediction accuracy to 97.11% and provides reliable recommendations for patients with cardiovascular disease.

• MeSH
• Algorithms * MeSH
• Humans MeSH
• Heart Diseases * diagnosis   MeSH
• Neural Networks, Computer MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH