Dynamic functional network connectivity in children with profound bilateral congenital sensorineural hearing loss.

BACKGROUND: Functional magnetic resonance imaging (fMRI) studies have revealed extensive functional reorganization in patients with sensorineural hearing loss (SNHL). However, almost no study focuses on the dynamic functional connectivity after hearing loss. OBJECTIVE: This study aimed to investigate dynamic functional connectivity changes in children with profound bilateral congenital SNHL under the age of 3 years. MATERIALS AND METHODS: Thirty-two children with profound bilateral congenital SNHL and 24 children with normal hearing were recruited for the present study. Independent component analysis identified 18 independent components composing five resting-state networks. A sliding window approach was used to acquire dynamic functional matrices. Three states were identified using the k-means algorithm. Then, the differences in temporal properties and the variance of network efficiency between groups were compared. RESULTS: The children with SNHL showed longer mean dwell time and decreased functional connectivity between the auditory network and sensorimotor network in state 3 (P < 0.05), which was characterized by relatively stronger functional connectivity between high-order resting-state networks and motion and perception networks. There was no difference in the variance of network efficiency. CONCLUSIONS: These results indicated the functional reorganization due to hearing loss. This study also provided new perspectives for understanding the state-dependent connectivity patterns in children with SNHL.

Unraveling the Neural Landscape of Mental Disorders using Double Functional Independent Primitives (dFIPs).

Mental illnesses extract a high personal and societal cost, and thus explorations of the links between mental illness and functional connectivity in the brain are critical. Investigating major mental illnesses, believed to arise from disruptions in sophisticated neural connections, allows us to comprehend how these neural network disruptions may be linked to altered cognition, emotional regulation, and social interactions. Although neuroimaging has opened new avenues to explore neural alterations linked to mental illnesses, the field still requires precise and sensitive methodologies to inspect these neural substrates of various psychological disorders. In this study, we employ a hierarchical methodology to derive double functionally independent primitives (dFIPs) from resting state functional magnetic resonance neuroimaging data (rs-fMRI). These dFIPs encapsulate canonical overlapping patterns of functional network connectivity (FNC) within the brain. Our investigation focuses on the examination of how combinations of these dFIPs relate to different mental disorder diagnoses. The central aim is to unravel the complex patterns of FNC that correspond to the diverse manifestations of mental illnesses. To achieve this objective, we used a large brain imaging dataset from multiple sites, comprising 5805 total individuals diagnosed with schizophrenia (SCZ), autism spectrum disorder (ASD), bipolar disorder (BPD), major depressive disorder (MDD), and controls. The key revelations of our study unveil distinct patterns associated with each mental disorder through the combination of dFIPs. Notably, certain individual dFIPs exhibit disorder-specific characteristics, while others demonstrate commonalities across disorders. This approach offers a novel, data-driven synthesis of intricate neuroimaging data, thereby illuminating the functional changes intertwined with various mental illnesses. Our results show distinct signatures associated with psychiatric disorders, revealing unique connectivity patterns such as heightened cerebellar connectivity in SCZ and sensory domain hyperconnectivity in ASD, both contrasted with reduced cerebellar-subcortical connectivity. Utilizing the dFIP concept, we pinpoint specific functional connections that differentiate healthy controls from individuals with mental illness, underscoring its utility in identifying neurobiological markers. In summary, our findings delineate how dFIPs serve as unique fingerprints for different mental disorders.

Altered effective connectivity of the default mode network in juvenile myoclonic epilepsy.

Juvenile myoclonic epilepsy (JME) is associated with brain dysconnectivity in the default mode network (DMN). Most previous studies of patients with JME have assessed static functional connectivity in terms of the temporal correlation of signal intensity among different brain regions. However, more recent studies have shown that the directionality of brain information flow has a more significant regional impact on patients' brains than previously assumed in the present study. Here, we introduced an empirical approach incorporating independent component analysis (ICA) and spectral dynamic causal modeling (spDCM) analysis to study the variation in effective connectivity in DMN in JME patients. We began by collecting resting-state functional magnetic resonance imaging (rs-fMRI) data from 37 patients and 37 matched controls. Then, we selected 8 key nodes within the DMN using ICA; finally, the key nodes were analyzed for effective connectivity using spDCM to explore the information flow and detect patient abnormalities. This study found that compared with normal subjects, patients with JME showed significant changes in the effective connectivity among the precuneus, hippocampus, and lingual gyrus (p < 0.05 with false discovery rate (FDR) correction) with most of the effective connections being strengthened. In addition, previous studies have found that the self-connection of normal subjects' nodes showed strong inhibition, but the self-connection inhibition of the anterior cingulate cortex and lingual gyrus of the patient was decreased in this experiment (p < 0.05 with FDR correction); as the activity in these areas decreased, the nodes connected to them all appeared abnormal. We believe that the changes in the effective connectivity of nodes within the DMN are accompanied by changes in information transmission that lead to changes in brain function and impaired cognitive and executive function in patients with JME. Overall, our findings extended the dysconnectivity hypothesis in JME from static to dynamic causal and demonstrated that aberrant effective connectivity may underlie abnormal brain function in JME patients at early phase of illness, contributing to the understanding of the pathogenesis of JME. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-023-09994-4.

Abnormal large-scale brain functional network dynamics in social anxiety disorder.

AIMS: Although static abnormalities of functional brain networks have been observed in patients with social anxiety disorder (SAD), the brain connectome dynamics at the macroscale network level remain obscure. We therefore used a multivariate data-driven method to search for dynamic functional network connectivity (dFNC) alterations in SAD. METHODS: We conducted spatial independent component analysis, and used a sliding-window approach with a k-means clustering algorithm, to characterize the recurring states of brain resting-state networks; then state transition metrics and FNC strength in the different states were compared between SAD patients and healthy controls (HC), and the relationship to SAD clinical characteristics was explored. RESULTS: Four distinct recurring states were identified. Compared with HC, SAD patients demonstrated higher fractional windows and mean dwelling time in the highest-frequency State 3, representing "widely weaker" FNC, but lower in States 2 and 4, representing "locally stronger" and "widely stronger" FNC, respectively. In State 1, representing "widely moderate" FNC, SAD patients showed decreased FNC mainly between the default mode network and the attention and perceptual networks. Some aberrant dFNC signatures correlated with illness duration. CONCLUSION: These aberrant patterns of brain functional synchronization dynamics among large-scale resting-state networks may provide new insights into the neuro-functional underpinnings of SAD.

Altered static and dynamic functional network connectivity in individuals with subthreshold depression: a large-scale resting-state fMRI study.

Dynamic functional network connectivity (dFNC) is an expansion of static FNC (sFNC) that reflects connectivity variations among brain networks. This study aimed to investigate changes in sFNC and dFNC strength and temporal properties in individuals with subthreshold depression (StD). Forty-two individuals with subthreshold depression and 38 healthy controls (HCs) were included in this study. Group independent component analysis (GICA) was used to determine target resting-state networks, namely, executive control network (ECN), default mode network (DMN), sensorimotor network (SMN) and dorsal attentional network (DAN). Sliding window and k-means clustering analyses were used to identify dFNC patterns and temporal properties in each subject. We compared sFNC and dFNC differences between the StD and HCs groups. Relationships between changes in FNC strength, temporal properties, and neurophysiological score were evaluated by Spearman's correlation analysis. The sFNC analysis revealed decreased FNC strength in StD individuals, including the DMN-CEN, DMN-SMN, SMN-CEN, and SMN-DAN. In the dFNC analysis, 4 reoccurring FNC patterns were identified. Compared to HCs, individuals with StD had increased mean dwell time and fraction time in a weakly connected state (state 4), which is associated with self-focused thinking status. In addition, the StD group demonstrated decreased dFNC strength between the DMN-DAN in state 2. sFNC strength (DMN-ECN) and temporal properties were correlated with HAMD-17 score in StD individuals (all p < 0.01). Our study provides new evidence on aberrant time-varying brain activity and large-scale network interaction disruptions in StD individuals, which may provide novel insight to better understand the underlying neuropathological mechanisms.

Evaluation of Brain Function Recovery After Traumatic Brain Injury Treatment in a Porcine Model by Cross-Group Temporal-Spatial Correlation Analysis.

Traumatic brain injury (TBI), a significant global health issue, is affecting ∼69 million annually. To better understand TBI's impact on brain function and assess the efficacy of treatments, this study uses a novel temporal-spatial cross-group approach with a porcine model, integrating resting-state functional magnetic resonance imaging (rs-fMRI) for temporal and arterial spin labeling for spatial information. Our research used 18 four-week-old pigs divided into three groups: TBI treated with saline (SLN, n = 6), TBI treated with fecal microbial transplant (FMT, n = 6), and a sham group (sham, n = 6) with only craniectomy surgery as the baseline. By applying machine learning techniques-specifically, independent component analysis and sparse dictionary learning-across seven identified resting-state networks, we assessed the temporal and spatial correlations indicative of treatment efficacy. Both temporal and spatial analyses revealed a consistent increase of correlation between the FMT and sham groups in the executive control and salience networks. Our results are further evidenced by a simulation study designed to mimic the progression of TBI severity through the introduction of variable Gaussian noise to an independent rs-fMRI dataset. The results demonstrate a decreasing temporal correlation between the sham and TBI groups with increasing injury severity, consistent with the experimental results. This study underscores the effectiveness of the methodology in evaluating post-TBI treatments such as the FMT. By presenting comprehensive experimental and simulated data, our research contributes significantly to the field and opens new paths for future investigations into TBI treatment evaluations.

Brain network functional connectivity changes in long illness duration chronic schizophrenia.

Introduction: Chronic schizophrenia has a course of 5 years or more and has a widespread abnormalities in brain functional connectivity. This study aimed to find characteristic functional and structural changes in a long illness duration chronic schizophrenia (10 years or more). Methods: Thirty-six patients with a long illness duration chronic schizophrenia and 38 healthy controls were analyzed by independent component analysis of brain network functional connectivity. Correlation analysis with clinical duration was performed on six resting state networks: auditory network, default mode network, dorsal attention network, fronto-parietal network, somatomotor network, and visual network. Results: The differences in the resting state network between the two groups revealed that patients exhibited enhanced inter-network connections between default mode network and multiple brain networks, while the inter-network connections between somatomotor network, default mode network and visual network were reduced. In patients, functional connectivity of Cuneus_L was negatively correlated with illness duration. Furthermore, receiver operating characteristic curve of functional connectivity showed that changes in Thalamus_L, Rectus_L, Frontal_Mid_R, and Cerebelum_9_L may indicate a longer illness duration chronic schizophrenia. Discussion: In our study, we also confirmed that the course of disease is significantly associated with specific brain regions, and the changes in specific brain regions may indicate that chronic schizophrenia has a course of 10 years or more.

Shredding artifacts: extracting brain activity in EEG from extreme artifacts during skateboarding using ASR and ICA.

Introduction: To understand brain function in natural real-world settings, it is crucial to acquire brain activity data in noisy environments with diverse artifacts. Electroencephalography (EEG), while susceptible to environmental and physiological artifacts, can be cleaned using advanced signal processing techniques like Artifact Subspace Reconstruction (ASR) and Independent Component Analysis (ICA). This study aims to demonstrate that ASR and ICA can effectively extract brain activity from the substantial artifacts occurring while skateboarding on a half-pipe ramp. Methods: A dual-task paradigm was used, where subjects were presented with auditory stimuli during skateboarding and rest conditions. The effectiveness of ASR and ICA in cleaning artifacts was evaluated using a support vector machine to classify the presence or absence of a sound stimulus in single-trial EEG data. The study evaluated the effectiveness of ASR and ICA in artifact cleaning using five different pipelines: (1) Minimal cleaning (bandpass filtering), (2) ASR only, (3) ICA only, (4) ICA followed by ASR (ICAASR), and (5) ASR preceding ICA (ASRICA). Three skateboarders participated in the experiment. Results: Results showed that all ICA-containing pipelines, especially ASRICA (69%, 68%, 63%), outperformed minimal cleaning (55%, 52%, 50%) in single-trial classification during skateboarding. The ASRICA pipeline performed significantly better than other pipelines containing ICA for two of the three subjects, with no other pipeline performing better than ASRICA. The superior performance of ASRICA likely results from ASR removing non-stationary artifacts, enhancing ICA decomposition. Evidenced by ASRICA identifying more brain components via ICLabel than ICA alone or ICAASR for all subjects. For the rest condition, with fewer artifacts, the ASRICA pipeline (71%, 82%, 75%) showed slight improvement over minimal cleaning (73%, 70%, 72%), performing significantly better for two subjects. Discussion: This study demonstrates that ASRICA can effectively clean artifacts to extract single-trial brain activity during skateboarding. These findings affirm the feasibility of recording brain activity during physically demanding tasks involving substantial body movement, laying the groundwork for future research into the neural processes governing complex and coordinated body movements.

A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia.

Over the past decade and a half, dynamic functional imaging has revealed low-dimensional brain connectivity measures, identified potential common human spatial connectivity states, tracked the transition patterns of these states, and demonstrated meaningful transition alterations in disorders and over the course of development. Recently, researchers have begun to analyze these data from the perspective of dynamic systems and information theory in the hopes of understanding how these dynamics support less easily quantified processes, such as information processing, cortical hierarchy, and consciousness. Little attention has been paid to the effects of psychiatric disease on these measures, however. We begin to rectify this by examining the complexity of subject trajectories in state space through the lens of information theory. Specifically, we identify a basis for the dynamic functional connectivity state space and track subject trajectories through this space over the course of the scan. The dynamic complexity of these trajectories is assessed along each dimension of the proposed basis space. Using these estimates, we demonstrate that schizophrenia patients display substantially simpler trajectories than demographically matched healthy controls and that this drop in complexity concentrates along specific dimensions. We also demonstrate that entropy generation in at least one of these dimensions is linked to cognitive performance. Overall, the results suggest great value in applying dynamic systems theory to problems of neuroimaging and reveal a substantial drop in the complexity of schizophrenia patients' brain function.

Adaptive Filtering with Fitted Noise Estimate (AFFiNE): Blink Artifact Correction in Simulated and Real P300 Data.

(1) Background: The electroencephalogram (EEG) is frequently corrupted by ocular artifacts such as saccades and blinks. Methods for correcting these artifacts include independent component analysis (ICA) and recursive-least-squares (RLS) adaptive filtering (-AF). Here, we introduce a new method, AFFiNE, that applies Bayesian adaptive regression spline (BARS) fitting to the adaptive filter's reference noise input to address the known limitations of both ICA and RLS-AF, and then compare the performance of all three methods. (2) Methods: Artifact-corrected P300 morphologies, topographies, and measurements were compared between the three methods, and to known truth conditions, where possible, using real and simulated blink-corrupted event-related potential (ERP) datasets. (3) Results: In both simulated and real datasets, AFFiNE was successful at removing the blink artifact while preserving the underlying P300 signal in all situations where RLS-AF failed. Compared to ICA, AFFiNE resulted in either a practically or an observably comparable error. (4) Conclusions: AFFiNE is an ocular artifact correction technique that is implementable in online analyses; it can adapt to being non-stationarity and is independent of channel density and recording duration. AFFiNE can be utilized for the removal of blink artifacts in situations where ICA may not be practically or theoretically useful.

A hybrid model for the classification of Autism Spectrum Disorder using Mu rhythm in EEG.

BACKGROUND: Autism Spectrum Disorder (ASD) is a condition with social interaction, communication, and behavioral difficulties. Diagnostic methods mostly rely on subjective evaluations and can lack objectivity. In this research Machine learning (ML) and deep learning (DL) techniques are used to enhance ASD classification. OBJECTIVE: This study focuses on improving ASD and TD classification accuracy with a minimal number of EEG channels. ML and DL models are used with EEG data, including Mu Rhythm from the Sensory Motor Cortex (SMC) for classification. METHODS: Non-linear features in time and frequency domains are extracted and ML models are applied for classification. The EEG 1D data is transformed into images using Independent Component Analysis-Second Order Blind Identification (ICA-SOBI), Spectrogram, and Continuous Wavelet Transform (CWT). RESULTS: Stacking Classifier employed with non-linear features yields precision, recall, F1-score, and accuracy rates of 78%, 79%, 78%, and 78% respectively. Including entropy and fuzzy entropy features further improves accuracy to 81.4%. In addition, DL models, employing SOBI, CWT, and spectrogram plots, achieve precision, recall, F1-score, and accuracy of 75%, 75%, 74%, and 75% respectively. The hybrid model, which combined deep learning features from spectrogram and CWT with machine learning, exhibits prominent improvement, attained precision, recall, F1-score, and accuracy of 94%, 94%, 94%, and 94% respectively. Incorporating entropy and fuzzy entropy features further improved the accuracy to 96.9%. CONCLUSIONS: This study underscores the potential of ML and DL techniques in improving the classification of ASD and TD individuals, particularly when utilizing a minimal set of EEG channels.

Densitometric analysis of GnRH and IBA1 immunocytochemistry in the basal ventromedial hypothalamus of the ewe.

Gonadotropin releasing hormone (GnRH) synthesis and secretion regulates seasonal fertility. In the brain, the distribution of GnRH-positive neurons is diffuse, hindering efforts to monitor variations in its cellular and tissue levels. Here, we aim at assessing GnRH immunoreactivity in nuclei responsible for seasonal fertility regulation (SFR) within the posterior, anterior, and preoptic areas of the basal hypothalamus during estrous in ewes. We detected reaction products in the ventromedial basal hypothalamus in neurons, nerve fibers, non-neuronal immunoreactive bodies, and diffuse interstitial areas. Immunoreactivity correlated with the distribution of the main SFR nuclei in the arcuate, retrochiasmatic, periventricular, medial preoptic, supraoptic, and preoptic areas. By independent component analysis density segmentation and by interferential contrast, we identified GnRH non-neuronal positive bodies as microglial cells encapsulated within a dense halo of reaction products. These GnRH-positive microglial cells were distributed in patches and rows throughout the basal ventromedial hypothalamus, suggesting their role in paracrine or juxtacrine signaling. Moreover, as shown by ionized calcium-binding adaptor molecule 1 (IBA1) immunocytochemistry, the distribution of GnRH reaction products overlapped with the microglial dense reactive zones. Therefore, our findings support the assertion that a combined densitometric analysis of GnRH and IBA1 immunocytochemistry enables activity mapping for monitoring seasonal changes following experimental interventions.

Structure-function interrelationships and associated neurotransmitter profiles in drug-naïve benign childhood epilepsy with central-temporal spikes patients.

OBJECTIVES: To explore the interrelationships between structural and functional changes as well as the potential neurotransmitter profile alterations in drug-naïve benign childhood epilepsy with central-temporal spikes (BECTS) patients. METHODS: Structural magnetic resonance imaging (sMRI) and resting-state functional MRI data from 20 drug-naïve BECTS patients and 33 healthy controls (HCs) were acquired. Parallel independent component analysis (P-ICA) was used to identify covarying components among gray matter volume (GMV) maps and fractional amplitude of low-frequency fluctuations (fALFF) maps. Furthermore, we explored the spatial correlations between GMV/fALFF changes derived from P-ICA and neurotransmitter maps in JuSpace toolbox. RESULTS: A significantly positive correlation (p < 0.001) was identified between one structural component (GMV_IC6) and one functional component (fALFF_IC4), which showed significant group differences between drug-naïve BECTS patients and HCs (GMV_IC6: p < 0.01; fALFF_IC4: p < 0.001). GMV_IC6 showed increased GMV in the frontal lobe, temporal lobe, thalamus, and precentral gyrus as well as fALFF_IC4 had enhanced fALFF in the cerebellum in drug-naïve BECTS patients compared to HCs. Moreover, significant correlations between GMV alterations in GMV_IC6 and the serotonin (5HT1a: p < 0.001; 5HT2a: p < 0.001), norepinephrine (NAT: p < 0.001) and glutamate systems (mGluR5: p < 0.001) as well as between fALFF alterations in fALFF_IC4 and the norepinephrine system (NAT: p < 0.001) were detected. CONCLUSION: The current findings suggest co-altered structural/functional components that reflect the correlation of language and motor networks as well as associated with the serotonergic, noradrenergic, and glutamatergic neurotransmitter systems. CLINICAL RELEVANCE STATEMENT: The relationship between anatomical brain structure and intrinsic neural activity was evaluated using a multimodal fusion analysis and neurotransmitters which might provide an important window into the multimodal neural and underlying molecular mechanisms of benign childhood epilepsy with central-temporal spikes. KEY POINTS: Structure-function relationships in drug-naïve benign childhood epilepsy with central-temporal spikes (BECTS) patients were explored. The interrelated structure-function components were found and correlated with the serotonin, norepinephrine, and glutamate systems. Co-altered structural/functional components reflect the correlation of language and motor networks and correlate with the specific neurotransmitter systems.

Adaptive EMG decomposition in dynamic conditions based on online learning metrics with tunable hyperparameters.

Objective. Developing neural decoders robust to non-stationary conditions is essential to ensure their long-term accuracy and stability. This is particularly important when decoding the neural drive to muscles during dynamic contractions, which pose significant challenges for stationary decoders.Approach. We propose a novel adaptive electromyography (EMG) decomposition algorithm that builds on blind source separation methods by leveraging the Kullback-Leibler divergence and kurtosis of the signals as metrics for online learning. The proposed approach provides a theoretical framework to tune the adaptation hyperparameters and compensate for non-stationarities in the mixing matrix, such as due to dynamic contractions, and to identify the underlying motor neuron (MN) discharges. The adaptation is performed in real-time (∼22 ms of computational time per 100 ms batches).Main results. The hyperparameters of the proposed adaptation captured anatomical differences between recording locations (forearm vs wrist) and generalised across subjects. Once optimised, the proposed adaptation algorithm significantly improved all decomposition performance metrics with respect to the absence of adaptation in a wide range of motion of the wrist (80∘). The rate of agreement, sensitivity, and precision were⩾90%in⩾80%of the cases in both simulated and experimentally recorded data, according to a two-source validation approach.Significance. The findings demonstrate the suitability of the proposed online learning metrics and hyperparameter optimisation to compensate the induced modulation and accurately decode MN discharges in dynamic conditions. Moreover, the study proposes an experimental validation method for EMG decomposition in dynamic tasks.

Longitudinal network-based brain grey matter MRI measures are clinically relevant and sensitive to treatment effects in multiple sclerosis.

In multiple sclerosis clinical trials, MRI outcome measures are typically extracted at a whole-brain level, but pathology is not homogeneous across the brain and so whole-brain measures may overlook regional treatment effects. Data-driven methods, such as independent component analysis, have shown promise in identifying regional disease effects but can only be computed at a group level and cannot be applied prospectively. The aim of this work was to develop a technique to extract longitudinal independent component analysis network-based measures of co-varying grey matter volumes, derived from T1-weighted volumetric MRI, in individual study participants, and assess their association with disability progression and treatment effects in clinical trials. We used longitudinal MRI and clinical data from 5089 participants (22 045 visits) with multiple sclerosis from eight clinical trials. We included people with relapsing-remitting, primary and secondary progressive multiple sclerosis. We used data from five negative clinical trials (2764 participants, 13 222 visits) to extract the independent component analysis-based measures. We then trained and cross-validated a least absolute shrinkage and selection operator regression model (which can be applied prospectively to previously unseen data) to predict the independent component analysis measures from the same regional MRI volume measures and applied it to data from three positive clinical trials (2325 participants, 8823 visits). We used nested mixed-effect models to determine how networks differ across multiple sclerosis phenotypes are associated with disability progression and to test sensitivity to treatment effects. We found 17 consistent patterns of co-varying regional volumes. In the training cohort, volume loss was faster in four networks in people with secondary progressive compared with relapsing-remitting multiple sclerosis and three networks with primary progressive multiple sclerosis. Volume changes were faster in secondary compared with primary progressive multiple sclerosis in four networks. In the combined positive trials cohort, eight independent component analysis networks and whole-brain grey matter volume measures showed treatment effects, and the magnitude of treatment-placebo differences in the network-based measures was consistently greater than with whole-brain grey matter volume measures. Longitudinal network-based analysis of grey matter volume changes is feasible using clinical trial data, showing differences cross-sectionally and longitudinally between multiple sclerosis phenotypes, associated with disability progression, and treatment effects. Future work is required to understand the pathological mechanisms underlying these regional changes.

Application of Independent Component Analysis and Nelder-Mead Particle Swarm Optimization Algorithm in Non-Contact Blood Pressure Estimation.

In recent years, hypertension has become one of the leading causes of illness and death worldwide. Changes in lifestyle among the population have led to an increasing prevalence of hypertension. This study proposes a non-contact blood pressure estimation method that allows patients to conveniently monitor their blood pressure values. By utilizing a webcam to track facial features and the region of interest (ROI) for obtaining forehead images, independent component analysis (ICA) is employed to eliminate artifact signals. Subsequently, physiological parameters are calculated using the principle of optical wave reflection. The Nelder-Mead (NM) simplex method is combined with the particle swarm optimization (PSO) algorithm to optimize the empirical parameters, thus enhancing computational efficiency and accurately determining the optimal solution for blood pressure estimation. The influences of light intensity and camera distance on the experimental results are also discussed. Furthermore, the measurement time is only 10 s. The superior accuracy and efficiency of the proposed methodology are demonstrated by comparing them with those in other published literature.

Abnormal Functional Connectivity Intra- and Inter-Network in Resting-State Brain Networks of Patients with Toothache.

Objective: To separate the resting-state network of patients with dental pain using independent component analysis (ICA) and analyze abnormal changes in functional connectivity within as well as between the networks. Patients and Methods: Twenty-three patients with dental pain and 30 healthy controls participated in this study. We extracted the resting-state functional network components of both using ICA. Functional connectivity differences within 14 resting-state brain networks were analyzed at the voxel level. Directional interactions between networks were analyzed using Granger causality analysis. Subsequently, functional connectivity values and causal coefficients were assessed for correlations with clinical parameters. Results: Compared to healthy controls, we found enhanced functional connectivity in the left superior temporal gyrus of anterior protrusion network and the right Rolandic operculum of auditory network in patients with dental pain (p<0.01 and cluster-level p<0.05, Gaussian random field corrected). In contrast, functional connectivity of the right precuneus in the precuneus network was reduced, and were significantly as well as negatively correlated to those of the Visual Analogue Scale (r=-4.93, p=0.017), Hamilton Anxiety Scale (r=-0.46, p=0.027), and Hamilton Depression Scale (r=-0.563, p<0.01), using the Spearman correlation analysis. Regarding the causal relationship between resting-state brain networks, we found increased connectivity from the language network to the precuneus in patients with dental pain (p<0.05, false discovery rate corrected). However, the increase in causal coefficients from the verbal network to the precuneus network was independent of clinical parameters. Conclusion: Patients with toothache exhibited abnormal functional changes in cognitive-emotion-related brain networks, such as the salience, auditory, and precuneus networks, thereby offering a new imaging basis for understanding central neural mechanisms in dental pain patients.

Altered functional connectivity of the default mode network in non-arteritic anterior ischaemic optic neuropathy.

The functional connectivity of the default mode network is important in understanding the neuro-pathophysiological abnormalities in patients with non-arteritic anterior ischaemic optic neuropathy. Independent component analysis can effectively determine within and between network connectivity of different brain components. Therefore, in order to explore the association between the default mode network and other brain regions, we utilized independent component analysis to investigate the alteration of functional connectivity of the default mode network. Thirty-one patients with non-arteritic anterior ischaemic optic neuropathy and 31 healthy controls, matched for age, sex and years of education, were recruited. For patients and healthy controls, functional connectivity within and between the default mode network and other brain regions were evaluated by independent component analysis. Compared with healthy controls, patients with non-arteritic anterior ischaemic optic neuropathy showed reduced functional connectivity within the default mode network in the right cerebellar tonsil and left cerebellum posterior lobe and increased functional connectivity in the left inferior temporal and right middle frontal gyri. Furthermore, patients with non-arteritic anterior ischaemic optic neuropathy showed reduced functional connectivity between the default mode network and other brain regions in the left cerebellar tonsil and increased functional connectivity in the right putamen, left thalamus, right middle temporal and left middle frontal gyri. In conclusion, negative correlations between several clinical parameters and functional connectivity of the default mode network were observed. The study contributes to understanding the mechanism of functional reorganization in non-arteritic anterior ischaemic optic neuropathy.

Deciphering nutritional stress responses via knowledge-enriched transcriptomics for microbial engineering.

Understanding diverse bacterial nutritional requirements and responses is foundational in microbial research and biotechnology. In this study, we employed knowledge-enriched transcriptomic analytics to decipher complex stress responses of Vibrio natriegens to supplied nutrients, aiming to enhance microbial engineering efforts. We computed 64 independently modulated gene sets that comprise a quantitative basis for transcriptome dynamics across a comprehensive transcriptomics dataset containing a broad array of nutrient conditions. Our approach led to the i) identification of novel transporter systems for diverse substrates, ii) a detailed understanding of how trace elements affect metabolism and growth, and iii) extensive characterization of nutrient-induced stress responses, including osmotic stress, low glycolytic flux, proteostasis, and altered protein expression. By clarifying the relationship between the acetate-associated regulon and glycolytic flux status of various nutrients, we have showcased its vital role in directing optimal carbon source selection. Our findings offer deep insights into the transcriptional landscape of bacterial nutrition and underscore its significance in tailoring strain engineering strategies, thereby facilitating the development of more efficient and robust microbial systems for biotechnological applications.

Elicitation of fetal ECG from abdominal recordings using Blind Source Separation techniques and Robust Set Membership Affine Projection algorithm for signal quality enhancement.

BACKGROUND: The utilization of non-invasive techniques for fetal cardiac health surveillance is pivotal in evaluating fetal well-being throughout the gestational period. This process requires clean and interpretable fetal Electrocardiogram (fECG) signals. METHOD: The proposed work is the novel framework for the elicitation of fECG signals from abdominal ECG (aECG) recordings of the pregnant mother. The comprehensive approach encompasses pre-processing of the raw ECG signal, Blind Source Separation techniques (BSS), Decomposition techniques like Empirical Mode Decomposition (EMD), and its variants like Ensemble Empirical Mode Decomposition (EEMD), and Complete Ensemble Empirical Mode Decomposition with Additive Noise (CEEMDAN). The Robust Set Membership Affine Projection (RSMAP) Algorithm is deployed for the enhancement of the obtained fECG signal. RESULT: The results show significant improvements in the elicited fECG signal with a maximum Signal Noise Ratio (SNR) of 31.72 dB and correlation coefficient = 0.899, Maximum Heart Rate(MHR) obtained in the range of 108-142 bpm for all the records of abdominal ECG signals. The statistical test gave a p-value of 0.21 accepting the null hypothesis. The Abdominal and Direct Fetal Electrocardiogram Database (ABDFECGDB) from PhysioNet has been used for this analysis. CONCLUSION: The proposed framework demonstrates a robust and effective method for the elicitation and enhancement of fECG signals from the abdominal recordings.