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

OBJECTIVE: Research suggests that disrupted interoception contributes to the development and maintenance of functional neurological disorder (FND); however, no functional neuroimaging studies have examined the processing of interoceptive signals in patients with FND. METHODS: The authors examined univariate and multivariate functional MRI neural responses of 38 patients with mixed FND and 38 healthy control individuals (HCs) during a task exploring goal-directed attention to cardiac interoception-versus-control (exteroception or rest) conditions. The relationships between interoception-related neural responses, heartbeat-counting accuracy, and interoceptive trait prediction error (ITPE) were also investigated for FND patients. RESULTS: When attention was directed to heartbeat signals versus exteroception or rest tasks, FND patients showed decreased neural activations (and reduced coactivations) in the right anterior insula and bilateral dorsal anterior cingulate cortices (among other areas), compared with HCs. For FND patients, heartbeat-counting accuracy was positively correlated with right anterior insula and ventromedial prefrontal activations during interoception versus rest. Cardiac interoceptive accuracy was also correlated with bilateral dorsal anterior cingulate activations in the interoception-versus-exteroception contrast, and neural activations were correlated with ITPE scores, showing inverse relationships to those observed for heartbeat-counting accuracy. CONCLUSIONS: This study identified state and trait interoceptive disruptions in FND patients. Convergent between- and within-group findings contextualize the pathophysiological role of cingulo-insular (salience network) areas across the spectrum of functional seizures and functional movement disorder. These findings provide a starting point for the future development of comprehensive neurophysiological assessments of interoception for FND patients, features that also warrant research as potential prognostic and monitoring biomarkers.

• MeSH
• Adult MeSH
• Interoception * physiology   MeSH
• Middle Aged MeSH
• Humans MeSH
• Magnetic Resonance Imaging MeSH
• Brain Mapping MeSH
• Young Adult MeSH
• Brain * physiopathology   diagnostic imaging   MeSH
• Nervous System Diseases * physiopathology   diagnostic imaging   MeSH
• Attention physiology   MeSH
• Heart Rate physiology   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Young Adult MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article 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

Accelerated epigenetic aging has been associated with changes in cognition. However, due to the lack of neuroimaging epigenetics studies, it is still unclear whether accelerated epigenetic. Aging in young adulthood might underlie the relationship between altered brain dynamics and cognitive functioning. We conducted neuroimaging epigenetics follow-up of the European Longitudinal Study of Pregnancy and Childhood (ELSPAC) prenatal birth cohort in young adulthood and tested the possible mediatory role of accelerated epigenetic aging in the relationship between dynamic functional connectivity (DFC) and worse cognition. A total of 240 young adults (51% men; 28-30 years, all of European ancestry) participated in the neuroimaging epigenetics follow-up. Buccal swabs were collected to assess DNA methylation and calculate epigenetic aging using Horvath's epigenetic clock. Full-scale IQ was assessed using the Wechsler adult intelligence scale (WAIS). Resting-state functional magnetic resonance imaging (rs-fMRI) was acquired using a 3T Siemens Prisma MRI scanner, and DFC was assessed using mixture factor analysis, revealing information about the coverage of different DFC states. In women (but not men), lower coverage of DFC state 4 and thus lower frequency of epochs with high connectivity within the default mode network and between default mode, fronto-parietal, and visual networks was associated with lower full-scale IQ (AdjR2 = 0.05, std. beta = 0.245, p = 0.008). This relationship was mediated by accelerated epigenetic aging (ab = 7.660, SE = 4.829, 95% CI [0.473, 19.264]). In women, accelerated epigenetic aging in young adulthood mediates the relationship between altered brain dynamics and cognitive functioning. Prevention of cognitive decline should target women already in young adulthood.

• MeSH
• Default Mode Network * diagnostic imaging   physiology   MeSH
• Adult MeSH
• Epigenesis, Genetic * physiology   MeSH
• Intelligence * physiology   MeSH
• Cognition * physiology   MeSH
• Connectome * MeSH
• Humans MeSH
• Longitudinal Studies MeSH
• Magnetic Resonance Imaging MeSH
• DNA Methylation MeSH
• Young Adult MeSH
• Brain * diagnostic imaging   physiology   MeSH
• Nerve Net * diagnostic imaging   physiology   MeSH
• Aging * physiology   genetics   MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Young Adult MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

Neural networks are responsible for processing sensory stimuli and driving the synaptic activity required for brain function and behavior. This computational capacity is expensive and requires a steady supply of energy and building blocks to operate. Importantly, the neural networks are composed of different cell populations, whose metabolic profiles differ between each other, thus endowing them with different metabolic capacities, such as, for example, the ability to synthesize specific metabolic precursors or variable proficiency to manage their metabolic waste. These marked differences likely prompted the emergence of diverse intercellular metabolic interactions, in which the shuttling and cycling of specific metabolites between brain cells allows the separation of workload and efficient control of energy demand and supply within the central nervous system. Nevertheless, our knowledge about brain bioenergetics and the specific metabolic adaptations of neural cells still warrants further studies. In this review, originated from the Fourth International Society for Neurochemistry (ISN) and Journal of Neurochemistry (JNC) Flagship School held in Schmerlenbach, Germany (2022), we describe and discuss the specific metabolic profiles of brain cells, the intercellular metabolic exchanges between these cells, and how these bioenergetic activities shape synaptic function and behavior. Furthermore, we discuss the potential role of faulty brain metabolic activity in the etiology and progression of Alzheimer's disease, Parkinson disease, and Amyotrophic lateral sclerosis. We foresee that a deeper understanding of neural networks metabolism will provide crucial insights into how higher-order brain functions emerge and reveal the roots of neuropathological conditions whose hallmarks include impaired brain metabolic function.

• MeSH
• Energy Metabolism * physiology   MeSH
• Humans MeSH
• Metabolic Networks and Pathways * physiology   MeSH
• Brain * metabolism   MeSH
• Nerve Net * metabolism   MeSH
• Neurons * metabolism   MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Review MeSH

Developmental remodeling shapes neural circuits via activity-dependent pruning of synapses and axons. Regulation of the cytoskeleton is critical for this process, as microtubule loss via enzymatic severing is an early step of pruning across many circuits and species. However, how microtubule-severing enzymes, such as spastin, are activated in specific neuronal compartments remains unknown. Here, we reveal that polyglutamylation, a post-translational tubulin modification enriched in neurons, plays an instructive role in developmental remodeling by tagging microtubules for severing. Motor neuron-specific gene deletion of enzymes that add or remove tubulin polyglutamylation-TTLL glutamylases vs. CCP deglutamylases-accelerates or delays neuromuscular synapse remodeling in a neurotransmission-dependent manner. This mechanism is not specific to peripheral synapses but also operates in central circuits, e.g., the hippocampus. Thus, tubulin polyglutamylation acts as a cytoskeletal rheostat of remodeling that shapes neuronal morphology and connectivity.

• MeSH
• Hippocampus metabolism   cytology   MeSH
• Polyglutamic Acid * metabolism   MeSH
• Microtubules * metabolism   MeSH
• Motor Neurons * metabolism   MeSH
• Mice MeSH
• Neuromuscular Junction metabolism   MeSH
• Synaptic Transmission MeSH
• Neurons * metabolism   MeSH
• Neuronal Plasticity * physiology   MeSH
• Peptide Synthases metabolism   genetics   MeSH
• Protein Processing, Post-Translational MeSH
• Spastin metabolism   MeSH
• Synapses metabolism   MeSH
• Tubulin metabolism   MeSH
• Animals MeSH
• Check Tag
• Mice MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH

PURPOSE: Fuchs endothelial corneal dystrophy (FECD) is a common, age-related cause of visual impairment. This systematic review synthesizes evidence from the literature on artificial intelligence (AI) models developed for the diagnosis and management of FECD. METHODS: We conducted a systematic literature search in MEDLINE, PubMed, Web of Science, and Scopus from January 1, 2000, to June 31, 2024. Full-text studies utilizing AI for various clinical contexts of FECD management were included. Data extraction covered model development, predicted outcomes, validation, and model performance metrics. We graded the included studies using the Quality Assessment of Diagnostic Accuracies Studies 2 tool. This review adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations. RESULTS: Nineteen studies were analyzed. Primary AI algorithms applied in FECD diagnosis and management included neural network architectures specialized for computer vision, utilized on confocal or specular microscopy images, or anterior segment optical coherence tomography images. AI was employed in diverse clinical contexts, such as assessing corneal endothelium and edema and predicting post-corneal transplantation graft detachment and survival. Despite many studies reporting promising model performance, a notable limitation was that only three studies performed external validation. Bias introduced by patient selection processes and experimental designs was evident in the included studies. CONCLUSIONS: Despite the potential of AI algorithms to enhance FECD diagnosis and prognostication, further work is required to evaluate their real-world applicability and clinical utility. TRANSLATIONAL RELEVANCE: This review offers critical insights for researchers, clinicians, and policymakers, aiding their understanding of existing AI research in FECD management and guiding future health service strategies.

• MeSH
• Fuchs' Endothelial Dystrophy * diagnosis   therapy   MeSH
• Humans MeSH
• Tomography, Optical Coherence methods   MeSH
• Artificial Intelligence * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Systematic Review MeSH

Achieving a reliable and accurate biomedical image segmentation is a long-standing problem. In order to train or adapt the segmentation methods or measure their performance, reference segmentation masks are required. Usually gold-standard annotations, i.e. human-origin reference annotations, are used as reference although they are very hard to obtain. The increasing size of the acquired image data, large dimensionality such as 3D or 3D + time, limited human expert time, and annotator variability, typically result in sparsely annotated gold-standard datasets. Reliable silver-standard annotations, i.e. computer-origin reference annotations, are needed to provide dense segmentation annotations by fusing multiple computer-origin segmentation results. The produced dense silver-standard annotations can then be either used as reference annotations directly, or converted into gold-standard ones with much lighter manual curation, which saves experts' time significantly. We propose a novel full-resolution multi-rater fusion convolutional neural network (CNN) architecture for biomedical image segmentation masks, called DeepFuse, which lacks any down-sampling layers. Staying everywhere at the full resolution enables DeepFuse to fully benefit from the enormous feature extraction capabilities of CNNs. DeepFuse outperforms the popular and commonly used fusion methods, STAPLE, SIMPLE and other majority-voting-based approaches with statistical significance on a wide range of benchmark datasets as demonstrated on examples of a challenging task of 2D and 3D cell and cell nuclei instance segmentation for a wide range of microscopy modalities, magnifications, cell shapes and densities. A remarkable feature of the proposed method is that it can apply specialized post-processing to the segmentation masks of each rater separately and recover under-segmented object parts during the refinement phase even if the majority of inputs vote otherwise. Thus, DeepFuse takes a big step towards obtaining fast and reliable computer-origin segmentation annotations for biomedical images.

• MeSH
• Humans MeSH
• Neural Networks, Computer * MeSH
• Image Processing, Computer-Assisted * methods   MeSH
• Check Tag
• Humans 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

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