Independent Component Analysis Research Articles

Dysconnectivity of the cerebellum and somatomotor network correlates with the severity of alogia in chronic schizophrenia

Recent fMRI resting-state findings show aberrant functional connectivity within somatomotor network (SMN) in schizophrenia. Moreover, functional connectivity aberrations of the motor system are often reported to be related to the severity of psychotic symptoms. Thus, it is important to validate those findings and confirm their relationship with psychopathology. Therefore, we decided to take an entirely data-driven approach in our fMRI resting-state study of 30 chronic schizophrenia outpatients and 30 matched control subjects. We used independent component analysis (ICA), dual regression, and seed-based connectivity analysis. We found reduced functional connectivity within SMN in schizophrenia patients compared to controls and SMN hypoconnectivity with the cerebellum in schizophrenia patients. The latter was strongly correlated with the severity of alogia, one of the main psychotic symptoms, i.e. poverty of speech and reduction in spontaneous speech,. Our results are consistent with the recent knowledge about the role of the cerebellum in cognitive functioning and its abnormalities in psychiatric disorders, e.g. schizophrenia. In conclusion, the presented results, for the first time clearly showed the involvement of the cerebellum hypoconnectivity with SMN in the persistence and severity of alogia symptoms in schizophrenia.

Static and Dynamic Dysconnectivity in Early Psychosis: Relationship With Symptom Dimensions.

Altered functional connectivity (FC) has been frequently reported in psychosis. Studying FC and its time-varying patterns in early-stage psychosis allows the investigation of the neural mechanisms of this disorder without the confounding effects of drug treatment or illness-related factors. We employed resting-state functional magnetic resonance imaging (rs-fMRI) to explore FC in individuals with early psychosis (EP), who also underwent clinical and neuropsychological assessments. 96 EP and 56 demographically matched healthy controls (HC) from the Human Connectome Project for Early Psychosis database were included. Multivariate analyses using spatial group independent component analysis were used to compute static FC and dynamic functional network connectivity (dFNC). Partial correlations between FC measures and clinical and cognitive variables were performed to test brain-behavior associations. Compared to HC, EP showed higher static FC in the striatum and temporal, frontal, and parietal cortex, as well as lower FC in the frontal, parietal, and occipital gyrus. We found a negative correlation in EP between cognitive function and FC in the right striatum FC (pFWE = 0.009). All dFNC parameters, including dynamism and fluidity measures, were altered in EP, and positive symptoms were negatively correlated with the meta-state changes and the total distance (pFWE = 0.040 and pFWE = 0.049). Our findings support the view that psychosis is characterized from the early stages by complex alterations in intrinsic static and dynamic FC, that may ultimately result in positive symptoms and cognitive deficits.

Multi-sensor fault detection and correction for automated IAQ monitoring in smart buildings through attention-aware autoencoders with spatial prediction module

The integration of interconnected monitoring and control devices has significantly enhanced the management of indoor building environments, notably in underground subway stations. In these sophisticated settings, the accuracy of monitoring systems is critical, as data-driven control systems rely extensively on sensor feedback. However, sensor faults arising from hardware damage, wear, and cyberattacks compromise the reliability of these complex systems. Despite extensive efforts, existing methods still face challenges in accurately detecting and reconstructing faults, particularly when dealing with multiple sensor failures. This study introduces a hybrid sensor validation framework that combines a multi-head attention-based denoising autoencoder (AE) with a deep learning prediction module. The performance assessment of the proposed gated recurrent unit (GRU) integrated attention-based fault detection, and reconstruction network (GADRN) shows a significant 33 % and 21 % improvement in critical success index compared to Independent Component Analysis and GRU-AE frameworks. GADRN consistently identified faulty sensors across various fault periods, achieving the lowest false alarm rate of 11 % against several conventional and machine learning-based models. A ventilation control analysis highlights the significance of incorporating a sensor validation framework in modern buildings. The outputs by the GADRN led to a 22.5 % decrease in energy consumption by preventing unnecessary resource use attributed to sensor faults.

Diesel Engine Turbocharger Monitoring by Processing Accelerometric Signals through Empirical Mode Decomposition and Independent Component Analysis

In this study, a method for the monitoring of internal combustion engine operation by vibration signals is proposed. The work falls within the context of the increasingly stringent standards relating to the environmental impact of engines and the development of monitoring and control techniques to ensure increased engine performance as well as fuel saving and reduction of pollutant emissions. Experimentation was performed on a turbocharged light-duty compression ignition direct-injection engine. Two monoaxial accelerometers were installed on the engine compressor case, the speed of which has been demonstrated to be closely related to the engine operation. Vibration measurements of the engine compressor case have been processed by combining the Empirical Mode Decomposition technique with Independent Component Analysis and Short Time Fourier Transform to indirectly estimate the turbocharger speed. The obtained traces have been compared to the direct turbocharger velocity measures during the stationary running of the engine (speed and load conditions varied in the complete engine’s range of operation). The results point out the potentiality of the methodology in algorithms devoted to identifying modifications of the combustion development regarding regular operation via indirect turbocharger speed monitoring.

Optimized Collective Variable for Collapse Transition in Linear Hydrophobic Polymers: Importance of Hydration Water and End-to-End Distance.

Choosing an appropriate collective variable (CV) for any biomolecular process is a challenging task. Researchers are developing methods to solve this issue using a variety of methodologies, most recently using machine learning (ML) methods. In this work, we investigate the mechanism of collapse transition across various lengths of polymer systems through adaptively sampled multiple short trajectories utilizing the Time Lagged Independent Component Analysis (TICA) framework. From TICA analysis, it is revealed that the radius of gyration (Rg) and end-to-end distance serve as good order parameters (OPs) for these systems describing overall energy landscapes. Markov state model (MSM) and mean first passage time (MFPT) analysis suggest that hydration water (Nw) plays a determining role in dictating the time scale and barrier for the collapsed transition for the C40 system. P-fold analysis on identifying transition state ensembles (TSE) identified by committor analysis also strengthens the role of Nw in such a transition. TICA, MSM, and committor analyses on the collapse transition for C45 reveal similarities with C40 systems in different aspects. Furthermore, we propose a pipeline integrating XGBoost regression along with an interpretable ML model, Shapley Additive exPlanation (SHAP) to precisely elucidate the contribution of each OP locally at the TSE. Through this approach, we observe that the collapse transition is primarily driven by Nw for both polymer systems. A carefully designed protocol for the collapsed transition of C60 systems indirectly reiterates the above result. Overall, our results suggest that while the end-to-end distance should be considered for better resolution of metastable states in the landscape, Nw is the crucial coordinate to be used in enhanced sampling for the exploration of actual collapse transitions for linear hydrophobic polymer systems. The Python code for analyzing the contribution of different OPs in the TSE using an ML-aided protocol is available on GitHub (https://github.com/saikat-ai/linear_polymer_project).

Breaking (musical) boundaries by investigating brain dynamics of event segmentation during real-life music-listening

The perception of musical phrase boundaries is a critical aspect of human musical experience: It allows us to organize, understand, derive pleasure from, and remember music. Identifying boundaries is a prerequisite for segmenting music into meaningful chunks, facilitating efficient processing and storage while providing an enjoyable, fulfilling listening experience through the anticipation of upcoming musical events. Expanding on Sridharan et al.'s [Neuron 55, 521-532 (2007)] work on coarse musical boundaries between symphonic movements, we examined finer-grained boundaries. We measured the fMRI responses of 18 musicians and 18 nonmusicians during music listening. Using general linear model, independent component analysis, and Granger causality, we observed heightened auditory integration in anticipation to musical boundaries, and an extensive decrease within the fronto-temporal-parietal network during and immediately following boundaries. Notably, responses were modulated by musicianship. Findings uncover the intricate interplay between musical structure, expertise, and cognitive processing, advancing our knowledge of how the brain makes sense of music.

Accurate Deformation Retrieval of the 2023 Turkey–Syria Earthquakes Using Multi-Track InSAR Data and a Spatio-Temporal Correlation Analysis with the ICA Method

Multi-track synthetic aperture radar interferometry (InSAR) provides a good approach for the monitoring of long-term multi-dimensional earthquake deformation, including pre-, co-, and post-seismic data. However, the removal of atmospheric errors in both single- and multi-track InSAR data presents significant challenges. In this paper, a method of spatio-temporal correlation analysis using independent component analysis (ICA) is proposed, which can extract multi-track deformation components for the accurate retrieval of earthquake deformation time series. Sentinel-1 data covering the double earthquakes in Turkey and Syria in 2023 are used to demonstrate the effectiveness of the proposed method. The results show that co-seismic displacement in the east–west and up–down directions ranged from −114.7 cm to 82.8 cm and from −87.0 cm to 63.9 cm, respectively. Additionally, the deformation rates during the monitoring period ranged from −137.9 cm/year to 123.3 cm/year in the east–west direction and from −51.8 cm/year to 45.7 cm/year in the up–down direction. A comparative validation experiment was conducted using three GPS stations. Compared with the results of the original MSBAS method, the proposed method provides results that are smoother and closer to those of the GPS data, and the average optimization efficiency is 43.08% higher. The experiments demonstrated that the proposed method could provide accurate two-dimensional deformation time series for studying the pre-, co-, and post-earthquake events of the 2023 Turkey–Syria Earthquakes.

Barrier Function to Skin Elasticity in Talking Head

In this paper, we target the problem of generating facial expressions from a piece of audio. This is challenging since both audio and video have inherent characteristics that are distinct from the other. Some words may have identical lip movements, and speech impediments may prevent lip-reading in some individuals. Previous approaches to generating such a talking head suffered from stiff expressions. This is because they focused only on lip movements and the facial landmarks did not contain the information flow from the audio. Hence, in this work, we employ spatio-temporal independent component analysis to accurately sync the audio with the corresponding face video. Proper word formation also requires control over the face muscles that can be captured using a barrier function. We first validated the approach on the diffusion of salt water in coastal areas using a synthetic finite element simulation. Next, we applied it to 3D facial expressions in toddlers for which training data is difficult to capture. Prior knowledge in the form of rules is specified using Fuzzy logic, and multi-objective optimization is used to collectively learn a set of rules. We observed significantly higher F-measure on three real-world problems.

Independent Vector Analysis for Feature Extraction in Motor Imagery Classification.

Independent vector analysis (IVA) can be viewed as an extension of independent component analysis (ICA) to multiple datasets. It exploits the statistical dependency between different datasets through mutual information. In the context of motor imagery classification based on electroencephalogram (EEG) signals for the brain-computer interface (BCI), several methods have been proposed to extract features efficiently, mainly based on common spatial patterns, filter banks, and deep learning. However, most methods use only one dataset at a time, which may not be sufficient for dealing with a multi-source retrieving problem in certain scenarios. From this perspective, this paper proposes an original approach for feature extraction through multiple datasets based on IVA to improve the classification of EEG-based motor imagery movements. The IVA components were used as features to classify imagined movements using consolidated classifiers (support vector machines and K-nearest neighbors) and deep classifiers (EEGNet and EEGInception). The results show an interesting performance concerning the clustering of MI-based BCI patients, and the proposed method reached an average accuracy of 86.7%.

A novel integrated method for improving the forecasting accuracy of crude oil: ESMD-CFastICA-BiLSTM-Attention

The volatility of crude oil price can significantly impact the stability of the crude oil market and even the global economy. Effectively predicting global crude oil price and volatility provides a scientific basis for decision-making for market regulators and investors worldwide, promoting the sound development of the global economy. In this study, we integrate signal processing with deep learning methods to present an optimal forecasting strategy for global crude oil price and volatility. We select the daily price of the WTI crude oil market from April 4, 1983, to December 12, 2023, for calculating volatility. Subsequently, employing extreme-point symmetric empirical mode decomposition (ESMD), K-means clustering, and fast independent component analysis method, we decompose and reconstruct the forecasting data, obtaining independent components with non-Gaussian characteristics. These components serve as inputs to estimate the accuracy of various models, including BiLSTM, Attention, LSTM, SVR, RF, and their combinations, in predicting crude oil price and volatility from both a point prediction and interval prediction perspective. Empirical results demonstrate that data decomposition, reconstruction, and the BiLSTM-Attention model outperform other models in predicting crude oil price and volatility.

The surface deformation of permafrost and active layer thickness in the upper reaches of the Black River basin, revealed by InSAR observations and independent component analysis

The Heihe River Basin, located in the northeastern part of the Qinghai-Tibetan Plateau, is part of the perennial permafrost belt of the Qilian Mountains. Recent observations indicate ongoing permafrost degradation in this region. This study utilizes data from 255 observations provided by Sentinel-1 satellites, MODIS Land Surface Temperature, SMAP-L4 soil moisture data, GNSS measurements, and in situ measurement. We introduced Variational Bayesian independent Component Analysis (VB-ICA) in multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) processing to investigate the spatial-temporal characteristics of surface deformation and permafrost active layer thickness (ALT) variations. The analysis demonstrates strong agreement with borehole data and offers improvements over traditional methodologies. The maximum value of ALT in the basin is found to be 5.7 m. VB-ICA effectively delineates seasonal deformations related to the freeze-thaw cycles, with a peak seasonal deformation amplitude of 60 mm. Moreover, the seasonal permafrost's lower boundary reaches an elevation of 3700 m, revealing that permafrost is experiencing widespread degradation and associated soil erosion in the high elevation region of The Heihe River Basin. The paper also explores the efficacy of reference point selection and baseline network establishment for employing the InSAR method in monitoring freeze-thaw deformations. The study underscores the InSAR method's adaptability and its importance for interpreting permafrost deformation and related parameters.

A novel adaptive weight bi-directional long short-term memory (AWBi-LSTM) classifier model for heart stroke risk level prediction in IoT.

Stroke prediction has become one of the significant research areas due to the increasing fatality rate. Hence, this article proposes a novel Adaptive Weight Bi-Directional Long Short-Term Memory (AWBi-LSTM) classifier model for stroke risk level prediction for IoT data. To efficiently train the classifier, Hybrid Genetic removes the missing data with Kmeans Algorithm (HKGA), and the data are aggregated. Then, the features are reduced with independent component analysis (ICA) to reduce the dataset size. After the correlated features are identified using the T-test-based uniform distribution-gradient search rule-based elephant herding optimization for cluster analysis (GSRBEHO) (T-test-UD-GSRBEHO). Next, the fuzzy rule-based decisions are created with the T-test-UDEHOA correlated features to classify the risk levels accurately. The feature values obtained from the fuzzy logic are given to the AWBi-LSTM classifier, which predicts and classifies the risk level of heart disease and diabetes. After the risk level is predicted, the data is securely stored in the database. Here, the MD5-Elliptic Curve Cryptography (MD5-ECC) technique is utilized for secure storage. Testing the suggested risk prediction model on the Stroke prediction dataset reveals potential efficacy. By obtaining an accuracy of 99.6%, the research outcomes demonstrated that the proposed model outperforms the existing techniques.

Heartbeat-related spectral perturbation of electroencephalogram reflects dynamic interoceptive attention states in the trial-by-trial classification analysis

Attending to heartbeats for interoceptive awareness initiates distinct electrophysiological responses synchronized with the R-peaks of an electrocardiogram (ECG), such as the heartbeat-evoked potential (HEP). Beyond HEP, this study proposes heartbeat-related spectral perturbation (HRSP), a time–frequency map of the R-peak locked electroencephalogram (EEG), and explores its characteristics in identifying interoceptive attention states using a classification approach. HRSPs of EEG brain components specified by independent component analysis (ICA) were used for the offline and online classification of interoceptive states. A convolutional neural network (CNN) designed specifically for HRSP was applied to publicly available data from a binary-state experiment (attending to self-heartbeats and white noise) and data from our four-state classification experiment (attending to self-heartbeats, white noise, time passage, and toe) with diverse input feature conditions of HRSP. From the dynamic state perspective, we evaluated the primary frequency bands of HRSP and the minimal number of averaging epochs required to reflect changing interoceptive attention states without compromising accuracy. We also assessed the utility of group ICA and models for classifying HRSP in new participants. The CNN for trial-by-trial HRSP with actual R-peaks demonstrated significantly higher classification accuracy than HRSP with sham, i.e., randomly positioned, R-peaks. Gradient-weighted class activation mapping highlighted the prominent role of theta and alpha bands between 200–600 ms post-R-peak—features absent in classifications using sham HRSPs. Online classification benefits from employing a group ICA and classification model, ensuring reliable accuracy without individual EEG precollection. These results suggest HRSP’s potential to reflect interoceptive attention states, proposing transformative implications for clinical applications.

Three layered sparse dictionary learning algorithm for enhancing the subject wise segregation of brain networks

Independent component analysis (ICA) and dictionary learning (DL) are the most successful blind source separation (BSS) methods for functional magnetic resonance imaging (fMRI) data analysis. However, ICA to higher and DL to lower extent may suffer from performance degradation by the presence of anomalous observations in the recovered time courses (TCs) and high overlaps among spatial maps (SMs). This paper addressed both problems using a novel three-layered sparse DL (TLSDL) algorithm that incorporated prior information in the dictionary update process and recovered full-rank outlier-free TCs from highly corrupted measurements. The associated sequential DL model involved factorizing each subject’s data into a multi-subject (MS) dictionary and MS sparse code while imposing a low-rank and a sparse matrix decomposition restriction on the dictionary matrix. It is derived by solving three layers of feature extraction and component estimation. The first and second layers captured brain regions with low and moderate spatial overlaps, respectively. The third layer that segregated regions with significant spatial overlaps solved a sequence of vector decomposition problems using the proximal alternating linearized minimization (PALM) method and solved a decomposition restriction using the alternating directions method (ALM). It learned outlier-free dynamics that integrate spatiotemporal diversities across brains and external information. It differs from existing DL methods owing to its unique optimization model, which incorporates prior knowledge, subject-wise/multi-subject representation matrices, and outlier handling. The TLSDL algorithm was compared with existing dictionary learning algorithms using experimental and synthetic fMRI datasets to verify its performance. Overall, the mean correlation value was found to be 26%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$26\\%$$\\end{document} higher for the TLSDL than for the state-of-the-art subject-wise sequential DL (swsDL) technique.

Improving the Sensitivity of Task-Based Multi-Echo Functional Magnetic Resonance Imaging via T2* Mapping Using Synthetic Data-Driven Deep Learning.

(1) Background: Functional magnetic resonance imaging (fMRI) utilizing multi-echo gradient echo-planar imaging (ME-GE-EPI) has demonstrated higher sensitivity and stability compared to utilizing single-echo gradient echo-planar imaging (SE-GE-EPI). The direct derivation of T2* maps from fitting multi-echo data enables accurate recording of dynamic functional changes in the brain, exhibiting higher sensitivity than echo combination maps. However, the widely employed voxel-wise log-linear fitting is susceptible to inevitable noise accumulation during image acquisition. (2) Methods: This work introduced a synthetic data-driven deep learning (SD-DL) method to obtain T2* maps for multi-echo (ME) fMRI analysis. (3) Results: The experimental results showed the efficient enhancement of the temporal signal-to-noise ratio (tSNR), improved task-based blood oxygen level-dependent (BOLD) percentage signal change, and enhanced performance in multi-echo independent component analysis (MEICA) using the proposed method. (4) Conclusion: T2* maps derived from ME-fMRI data using the proposed SD-DL method exhibit enhanced BOLD sensitivity in comparison to T2* maps derived from the LLF method.

Implementing nonlinear optics from Off-Energy closed orbit at NSLS-II

To characterize the second-order (chromatic sextupole) magnet lattice with high precision, we implemented nonlinear optics from off-energy closed orbit (NOECO) tool based on the linear optics from closed orbit modulation (LOCOM) method, named LOCOM-NOECO. The preliminary numerical study indicates that 1–2% precision can be achieved for the calibration of chromatic sextupoles. This accuracy could potentially help in resolving some long-standing challenges of NSLS-II (e.g., the discrepancy between the designed and measured dynamic apertures) if such high precision can be fulfilled. As an independent crosscheck, we also implemented NOECO based on the independent component analysis (ICA) method using turn-by-turn (TBT) BPM data, named ICA-NOECO. Both ICA-NOECO and LOCOM-NOECO have been successfully applied to identify the pre-dialed random errors of a chromatic sextupole family including five power supplies, and achieved the root mean square (RMS) residual error of 1% and peak error less than 2%. Moreover, to mitigate the chromatic sextupole error effect, we applied the correction and achieved significant improvements in the injection efficiency as well as the dynamic apertures.

Leveraging Multi-Echo EPI to Enhance BOLD Sensitivity in Task-based Olfactory fMRI.

Functional magnetic resonance imaging (fMRI) using blood-oxygenation-level-dependent (BOLD) contrast relies on gradient echo echo-planar imaging (GE-EPI) to quantify dynamic susceptibility changes associated with the hemodynamic response to neural activity. However, acquiring BOLD fMRI in human olfactory regions is particularly challenging due to their proximity to the sinuses where large susceptibility gradients induce magnetic field distortions. BOLD fMRI of the human olfactory system is further complicated by respiratory artifacts that are highly correlated with event onsets in olfactory tasks. Multi-echo EPI (ME-EPI) acquires gradient echo data at multiple echo times (TEs) during a single acquisition and can leverage signal evolution over the multiple echo times to enhance BOLD sensitivity and reduce artifactual signal contributions. In the current study, we developed a ME-EPI acquisition protocol for olfactory task-based fMRI and demonstrated significant improvement in BOLD signal sensitivity over conventional single-echo EPI (1E-EPI). The observed improvement arose from both an increase in BOLD signal changes through a T 2 * -weighted echo combination and a reduction in non-BOLD artifacts through the application of the Multi-Echo Independent Components Analysis (ME-ICA) denoising method. This study represents one of the first direct comparisons between 1E-EPI and ME-EPI in high-susceptibility regions and provides compelling evidence in favor of using ME-EPI for future task-based fMRI studies.

Imaging and anesthesia protocol optimization in sedated clinical resting state fMRI.

The quality of resting-state functional MRI (rs-fMRI) under anesthesia is variable and there are no guidelines on optimal image acquisition or anesthesia protocol. We aim to identify the factors that may lead to compromised clinical rs-fMRI under anesthesia. In this cross-sectional study, we analyzed clinical rs-fMRI data acquired under anesthesia from 2009-2023 at Massachusetts General Hospital. Independent component analysis driven resting state networks (RSN) of each patient were evaluated qualitatively and quantitatively and grouped as robust or weak. Overall networks were evaluated using the qualitative method, and motor and language networks were evaluated using the quantitative method. RSN robustness was analyzed in 4 outcome categories: overall, combined Motor-Language, individual motor, and language networks. Predictor variables included rs-fMRI acquisition parameters, anesthesia medications, underlying brain structural abnormalities, age, and sex. Logistic regression was used to examine the effect of the study variables on RSN robustness. Sixty-nine patients were identified. With qualitative assessment, 40 had robust and 29 had weak overall RSN. Quantitatively, 45 patients had robust, while 24 had weak Motor-Language networks. Among all the predictor variables, only sevoflurane significantly contributed to the outcomes, with sevoflurane administration reducing the odds of having robust RSN in overall (Odds Radio (OR)= 0.2, 95% Confidence Interval (CI) = [0.05;0.79], p = .02), Motor-Language (OR = 0.18, 95% CI = [0.04;0.80], p = .02) and individual motor (OR= 0.1, 95% CI = [0.02;0.64], p= .02) categories. Individual language network robustness was not associated with the tested predictor variables. Sevoflurane anesthesia may compromise the visibility of fMRI resting state networks, particularly impacting motor networks. This finding suggests that the type of anesthesia is a critical factor in rs-fMRI quality. We did not observe the association of the MR acquisition technique or underlying structural abnormality with the RSN robustness. BOLD = Blood Oxygen Level-Dependent; ICA = Independent Component Analysis; Rs-fMRI = Resting-State Functional Magnetic Resonance Imaging; RSN = Resting-State Networks; SNR = Signal-to-Noise Ratio.

Altered Function and Structure of the Cerebellum Associated with Gut-Brain Regulation in Crohn's Disease: a Structural and Functional MRI Study.

This study employed structural and functional magnetic resonance imaging (MRI) to investigate changes in the function and structure of the cerebellum associated with gut-brain axis (GBA) regulation in patients diagnosed with Crohn's disease (CD). The study comprised 20 CD patients, including 12 with active disease (CD-A) and 8 in remission (CD-R), as well as 21 healthy controls. Voxel-based morphometry (VBM) was utilized for structural analysis of cerebellar gray matter volume, while independent component analysis (ICA) was applied for functional analysis of cerebellar functional connectivity (FC). The results showed significant GMV reduction in the left posterior cerebellar lobe across all CD patients compared to HCs, with more pronounced differences in the CD-A subgroup. Additionally, an increase in mean FC of the cerebellar network was observed in all CD patients, particularly in the CD-A subgroup, which demonstrated elevated FC in the vermis and bilateral posterior cerebellum. Correlation analysis revealed a positive relationship between cerebellar FC and the Crohn's Disease Activity Index (CDAI) and a trend toward a negative association with the reciprocal of the Self-rating Depression Scale (SDS) score in CD patients. The study's findings suggest that the cerebellum may play a role in the abnormal regulation of the GBA in CD patients, contributing to a better understanding of the neural mechanisms underlying CD and highlighting the cerebellum's potential role in modulating gut-brain interactions.

A hybrid machine learning-powered intelligent system for enhancing dengue patient safety and care

Dengue fever is a significant global health concern, with millions of cases reported each year, leading to considerable morbidity and mortality. Early diagnosis, patient monitoring and timely intervention are crucial for managing dengue patients. This study proposed a hybrid machine learning-powered intelligent system designed to enhance dengue patients safety and care. Utilizing data provided at enrollment, including platelet, age, white cell, genders, hematocrit and lymphocyte counts, a Weighted K Nearest Neighbor fused Gradient Boosting Decision Tree (WKNN-GBDT) was utilized to forecast the ultimate diagnosis. The study included 50 patients recruited between July to October 2019 and diagnosed with dengue infection. The collected data was preprocessed and features extracted using min max normalization and independent component analysis (ICA). The WKNN-GBDT model had an overallprecision of 0.96%, f1-score of 0.90% accuracy of 0.98%, andrecall of 0.99% in predicting the final diagnosis. As a consequence of seasonality and other variables, model results changed over time. These models might enhance medical decision-making in the field of medical care and offer passive surveillance in dengue affected areas. Considering the unexpected consequences of human-induced climate change and its impact on health, as well as the implications of seasonality and shifting disease prevalence, is crucial.