Explore the vast range of titles available.

Schizophrenia (SZ) is a severe mental disorder characterized by failure of functional integration (aka dysconnection) across the brain. Recent functional connectivity (FC) studies have adopted functional parcellations to define subnetworks of large‐scale networks, and to characterize the (dys)connection between them, in normal and clinical populations. While FC examines statistical dependencies between observations, model‐based effective connectivity (EC) can disclose the causal influences that underwrite the observed dependencies. In this study, we investigated resting state EC within seven large‐scale networks, in 66 SZ and 74 healthy subjects from a public dataset. The results showed that a remarkable 33% of the effective connections (among subnetworks) of the cognitive control network had been pathologically modulated in SZ. Further dysconnection was identified within the visual, default mode and sensorimotor networks of SZ subjects, with 24%, 20%, and 11% aberrant couplings. Overall, the proportion of discriminative connections was remarkably larger in EC (24%) than FC (1%) analysis. Subsequently, to study the neural correlates of impaired cognition in SZ, we conducted a canonical correlation analysis between the EC parameters and the cognitive scores of the patients. As such, the self‐inhibitions of supplementary motor area and paracentral lobule (in the sensorimotor network) and the excitatory connection from parahippocampal gyrus to inferior temporal gyrus (in the cognitive control network) were significantly correlated with the social cognition, reasoning/problem solving and working memory capabilities of the patients. Future research can investigate the potential of whole‐brain EC as a biomarker for diagnosis of brain disorders and for neuroimaging‐based cognitive assessment. Effective connectivity analysis revealed that the cognitive control network is the most dysconnected network in schizophrenia, followed by the visual, default mode, sensorimotor and auditory networks. Moreover, dysconnections within the sensorimotor and cognitive control networks in schizophrenia patients were significantly correlated with the severity of their (social and nonsocial) cognitive impairments.

Emotion regulation plays a key role in human behavior and overall well-being. Neurofeedback is a non-invasive self-brain training technique used for emotion regulation to enhance brain function and treatment of mental disorders through behavioral changes. Previous neurofeedback research often focused on using activity from a single brain region as measured by fMRI or power from one or two EEG electrodes. In a new study, we employed connectivity-based EEG neurofeedback through recalling positive autobiographical memories and simultaneous fMRI to upregulate positive emotion. In our novel approach, the feedback was determined by the coherence of EEG electrodes rather than the power of one or two electrodes. We compared the efficiency of this connectivity-based neurofeedback to traditional activity-based neurofeedback through multiple experiments. The results showed that connectivity-based neurofeedback effectively improved BOLD signal change and connectivity in key emotion regulation regions such as the amygdala, thalamus, and insula, and increased EEG frontal asymmetry, which is a biomarker for emotion regulation and treatment of mental disorders such as PTSD, anxiety, and depression and coherence among EEG channels. The psychometric evaluations conducted both before and after the neurofeedback experiments revealed that participants demonstrated improvements in enhancing positive emotions and reducing negative emotions when utilizing connectivity-based neurofeedback, as compared to traditional activity-based and sham neurofeedback approaches. These findings suggest that connectivity-based neurofeedback may be a superior method for regulating emotions and could be a useful alternative therapy for mental disorders, providing individuals with greater control over their brain and mental functions.

While respiration is known to rhythmically modulate brain activity, how different breathing modes (nasal vs. oral) affect frequency-specific large-scale neural connectivity in humans remains unexplored. We used resting-state functional magnetic resonance imaging (fMRI) to examine how nasal and oral breathing modulate functional brain connectivity, focusing on blood oxygenation level-dependent (BOLD) fluctuations in the intermediate frequency band of 0.1–0.2 Hz in 20 healthy male participants. A fully data-driven ROI-based inference approach across 133 whole-brain ROIs revealed that nasal and oral breathing significantly activated the olfactory region and brainstem, respectively. Seed-based connectivity (SBC) analysis, using nonparametric permutation testing (10,000 iterations) and cluster-wise false discovery rate (FDR) thresholding (p-FDR < 0.05), based on these seeds, revealed distinct patterns of network engagement depending on breathing mode. Nasal breathing was associated with greater functional connectivity within higher-order brain networks, including the salience, somatosensory, default mode, and frontoparietal networks. Conversely, oral breathing increased connectivity centered on the brainstem, engaging subcortical regions involved in autonomic regulation and survival functions. Despite these differences, both conditions recruited stable respiratory core regions comprising the hippocampus, amygdala, and insula. These findings suggest a novel framework, the respiration-entrained brain oscillation network (REBON), defined by three operational criteria: (1) it is frequency-specific to the 0.1–0.2 Hz band (centered around ~0.16 Hz); (2) the activity of its principal regions, the olfactory region and brainstem, alternates in dominance depending on the mode of breathing; and (3) it includes a stable core of limbic and interoceptive structures, such as the hippocampus, amygdala, and insula. Understanding this network may have implications for future therapeutic strategies aimed at supporting cognitive functions, emotion regulation, and the integrity of large-scale brain networks in both clinical and wellness contexts; however, these translational implications require validation in future experimental studies.

In the present study, a new algorithm for automatic target detection (ATR) in synthetic aperture radar (SAR) images has been proposed. First, moving and stationary target acquisition and recognition image chips have been segmented and then passed to a number of preprocessing stages such as histogram equalisation, position and size normalisation. Second, the feature extraction based on Zernike moments (ZMs) having linear transformation invariance properties and robustness in the presence of the noise has been introduced for the first time. Third, a genetic algorithm-based feature selection and a support vector machine classifier have been presented to select the optimal feature subset of ZMs for decreasing the computational complexity. Experimental results demonstrate the efficiency of the proposed approach in target recognition of SAR imagery. The authors obtained results show that just a small amount of ZMs features is sufficient to achieve the recognition rates that rival other established methods, and so ZMs features can be regarded as a powerful discriminatory feature for automatic target recognition applications relevant to SAR imagery. Furthermore, it can be observed that the classifier performs fairly well until the signal-to-noise ratio falls beneath 5 dB for noisy images.

Despite the existence of several emotion regulation studies using neurofeedback, interactions among a small number of regions were evaluated, and therefore, further investigation is needed to understand the interactions of the brain regions involved in emotion regulation. We implemented electroencephalography (EEG) neurofeedback with simultaneous functional magnetic resonance imaging (fMRI) using a modified happiness-inducing task through autobiographical memories to upregulate positive emotion. Then, an explorative analysis of whole brain regions was done to understand the effect of neurofeedback on brain activity and the interaction of whole brain regions involved in emotion regulation. The participants in the control and experimental groups were asked to do emotion regulation while viewing positive images of autobiographical memories and getting sham or real (based on alpha asymmetry) EEG neurofeedback, respectively. The proposed multimodal approach quantified the effects of EEG neurofeedback in changing EEG alpha power, fMRI blood oxygenation level-dependent (BOLD) activity of prefrontal, occipital, parietal, and limbic regions (up to 1.9% increase), and functional connectivity in/between prefrontal, parietal, limbic system, and insula in the experimental group. New connectivity links were identified by comparing the brain functional connectivity between experimental conditions (Upregulation and View blocks) and also by comparing the brain connectivity of the experimental and control groups. Psychometric assessments confirmed significant changes in positive and negative mood states in the experimental group by neurofeedback. Based on the exploratory analysis of activity and connectivity among all brain regions involved in emotion regions, we found significant BOLD and functional connectivity increases due to EEG neurofeedback in the experimental group, but no learning effect was observed in the control group. The results reveal several new connections among brain regions as a result of EEG neurofeedback which can be justified according to emotion regulation models and the role of those regions in emotion regulation and recalling positive autobiographical memories.

Joint Analysis of EEG and fMRI datasets can bring new insight into brain mechanisms. In this paper, we employed the recently introduced Correlated Coupled Tensor Matrix Factorization (CCMTF) method for analysis of the emotion regulation paradigm based on EEG frontal asymmetry neurofeedback in the alpha frequency band with simultaneous fMRI. CCMTF method assumes that the co-variations of the common dimension (temporal dimension) between EEG and fMRI are correlated and not necessarily identical. The results of the CCMTF method suggested that EEG and fMRI had similar covariations during the transition of brain activities from resting states to task (view and upregulation) states and these covariations followed an increasing trend. The fMRI shared spatial component showed activations in the limbic system, DLPFC, OFC, and VLPC regions, which were consistent with the previous studies and were linked to EEG frequency patterns in the range of 1-15 Hz with a correlation value close to 0.75. The estimated regions from the CCMTF method were then used as the candidate nodes for dynamic functional connectivity (dFC) analysis, in which the changes in connectivity from view to upregulation states were examined. The results of the dFC analysis were compared with a Normalized Mutual information (NMI) based approach in two different frequency ranges (1-15 Hz and 15-40 Hz) as the NMI method was applied to the vectors of dynamic functional connectivity nodes of EEG and fMRI data. The results of the two methods illustrated that the relation between EEG and fMRI datasets was mostly in the frequency range of 1-15 Hz. These relations were both in the brain activations and the dFCs between the two modalities. This paper suggests that the CCMTF method is a capable approach for extracting the shared information between EEG and fMRI data and can reveal new information about brain functions and their connectivity without solving the EEG inverse problem or analyzing different frequency bands.

Exploring brain networks is an essential step towards understanding functional organization of the brain, which needs characterization of linear and nonlinear connections based on measurements like EEG or MEG. Conventional measures of connectivity are mostly linear and bivariate. This paper proposes an effective connectivity measure called Adaptive Neuro-Fuzzy Inference System Granger Causality (ANFISGC). The proposed measure is based on the symplectic geometry embedding dimension, Adaptive Neuro-Fuzzy Inference System (ANFIS) predictor, and Granger Causality (GC). It is a powerful predictor that detects both linear and nonlinear causal information flow. It is not bivariate and thus can distinguish between direct and indirect connections. The performance of the proposed method is evaluated and compared with those of the Linear Granger Causality (LGC), Kernel Granger Causality (KGC), combination of Pairwise Granger Causality and Conditional Granger Causality (PwGC + CGC), Transfer Entropy (TE), and Phase Transfer Entropy (PTE) methods using simulated and experimental MEG data. Simulation results show that ANFISGC outperforms the other methods in detecting both linear and nonlinear connections and, by increasing the coupling strength between nodes, the value of ANFISGC increases. In the analysis of the time series of the brain sources of epilepsy patients obtained from the MEG inverse problem, the regions found by ANFISGC were more similar to the clinical findings than those found by the other methods.

Divisive normalization of the neural responses by the activity of the neighboring neurons has been proposed as a fundamental operation in the nervous system based on its success in predicting neural responses recorded in primate electrophysiology studies. Nevertheless, experimental evidence for the existence of this operation in the human brain is still scant. Here, using functional MRI, we examined the role of normalization across the visual hierarchy in the human visual cortex. Using stimuli form the two categories of human bodies and houses, we presented objects in isolation or in clutter and asked participants to attend or ignore the stimuli. Focusing on the primary visual area V1, the object-selective regions LO and pFs, the body-selective region EBA, and the scene-selective region PPA, we first modeled single-voxel responses using a weighted sum, a weighted average, and a normalization model and demonstrated that although the weighted sum and weighted average models also made acceptable predictions in some conditions, the response to multiple stimuli could generally be better described by a model that takes normalization into account. We then determined the observed effects of attention on cortical responses and demonstrated that these effects were predicted by the normalization model, but not by the weighted sum or the weighted average models. Our results thus provide evidence that the normalization model can predict responses to objects across shifts of visual attention, suggesting the role of normalization as a fundamental operation in the human brain.

Understanding pyro-convective clouds is essential. These clouds transport significant quantities of aerosols and gases into the upper atmosphere and therefore influence atmospheric composition, weather, and climate on a global scale. This study investigates the dynamics of pyro-convective clouds during the Australian New Years Event 2019/2020 using convection-resolving simulations that incorporate the effects of sensible heat and moisture released by fires. These effects are modeled through parameterizations using retrievals from the Global Fire Assimilation System (GFAS). The results show that the plume top height remains unchanged when accounting for fire-induced heat and moisture release in regions where convective cells form independently of the fire. In areas with the most intense fires, the sensible heat and moisture release from the fire provide the necessary buoyancy for enabling the formation of pyro-convective clouds. Pyro-convective clouds lift aerosol masses up to altitudes of 12.0 km. During their formation, the plume top height more than doubles, compared with a reference simulation in which such clouds do not develop. Additionally, the plume height increase is, on average, just 0.87 km by fire-induced heat and moisture in cloud-free areas. We demonstrated that sensible heat release is the primary contributor to pyro-convective cloud formation. However, the release of moisture enhances the formation process and increases the lifetime of the pyro-convective cloud. Comparisons with observational data show that the plume's distribution and height are underestimated. However, the simulations align well with observations after a 5–6 h delay, indicating that pyro-convective cells are accurately modeled but occur later in the model than are actually observed.

Cirrus clouds cover 25% of the Earth at any given time. However, significant uncertainties remain in our understanding of cirrus cloud formation, in particular, how it is impacted by aerosols. This study investigates the formation and properties of dust-induced cirrus clouds using long-term observational datasets, focusing on Central Asia’s Aral Sea region and the Iberian Peninsula. We identify cirrus events influenced by mineral dust using an algorithm that uses CALIPSO satellite data through spatial and temporal proximity analysis. Results indicate significant seasonal and regional variations in the prevalence of dust-induced cirrus clouds, with spring emerging as the peak season for the Aral Sea and high-altitude Saharan dust transport influencing the Iberian Peninsula. With the help of DARDAR-Nice data, we characterize dust-induced cirrus clouds as being thicker, forming at higher altitudes, and exhibiting distinct microphysical properties, including reduced ice crystal concentrations and smaller frozen water content. Furthermore, a statistical test using a non-parametric Mann–Whitney U test is employed and confirms the robustness of the study. These findings enhance our understanding of the interactions between mineral dust and cloud microphysics, with implications for global climate modeling and weather forecasting. This study provides methodological advancements for dust-induced cloud detection and highlights the need for integrating a dust–cloud feedback mechanism in weather and climate models.