Electrophysiology of human iPSC-derived vascular smooth muscle cells and cell autonomous consequences of Cantu Syndrome mutations.

OBJECTIVE: Cantu Syndrome (CS), a multisystem disease with a complex cardiovascular phenotype, is caused by GoF variants in the Kir6.1/SUR2 subunits of ATP-sensitive potassium (KATP) channels, and is characterized by low systemic vascular resistance, as well as tortuous, dilated vessels, and decreased pulse-wave velocity. Thus, CS vascular dysfunction is multifactorial, with both hypomyotonic and hyperelastic components. To dissect whether such complexities arise cell-autonomously within vascular smooth muscle cells (VSMCs), or as secondary responses to the pathophysiological milieu, we assessed electrical properties and gene expression in human induced pluripotent stem cell-derived VSMCs (hiPSC-VSMCs), differentiated from control and CS patient-derived hiPSCs, and in native mouse control and CS VSMCs. APPROACH AND RESULTS: Whole-cell voltage-clamp of isolated aortic and mesenteric arterial VSMCs isolated from wild type (WT) and Kir6.1[V65M] (CS) mice revealed no clear differences in voltage-gated K+ (Kv) or Ca2+ currents. Kv and Ca2+ currents were also not different between validated hiPSC-VSMCs differentiated from control and CS patient-derived hiPSCs. While pinacidil-sensitive KATP currents in control hiPSC-VSMCs were consistent with those in WT mouse VSMCs, they were considerably larger in CS hiPSC-VSMCs. Under current-clamp conditions, CS hiPSC-VSMCs were also hyperpolarized, consistent with increased basal K conductance, and providing an explanation for decreased tone and decreased vascular resistance in CS. Increased compliance was observed in isolated CS mouse aortae, and was associated with increased elastin mRNA expression. This was consistent with higher levels of elastin mRNA in CS hiPSC-VSMCs, suggesting that the hyperelastic component of CS vasculopathy is a cell-autonomous consequence of vascular KATP GoF. CONCLUSIONS: The results show that hiPSC-VSMCs reiterate expression of the same major ion currents as primary VSMCs, validating the use of these cells to study vascular disease. Results in hiPSC-VSMCs derived from CS patient cells suggest that both the hypomyotonic and hyperelastic components of CS vasculopathy are cell-autonomous phenomena driven by KATP overactivity within VSMCs.

The inotropic and arrhythmogenic effects of acutely increased late INa are associated with elevated ROS but not oxidation of PKARIα.

Background: Acute stimulation of the late sodium current (INaL) as pharmacologically induced by Anemonia toxin II (ATX-II) results in Na+-dependent Ca2+ overload and enhanced formation of reactive oxygen species (ROS). This is accompanied by an acute increase in the amplitude of the systolic Ca2+ transient. Ca2+ transient amplitude is determined by L-type Ca2+-mediated transsarcolemmal Ca2+ influx (ICa) into the cytosol and by systolic Ca2+ release from the sarcoplasmic reticulum (SR). Type-1 protein kinase A (PKARIα) becomes activated upon increased ROS and is capable of stimulating ICa, thereby sustaining the amplitude of the systolic Ca2+ transient upon oxidative stress. Objectives: We aimed to investigate whether the increase of the systolic Ca2+ transient as acutely induced by INaL (by ATX-II) may involve stimulation of ICa through oxidized PKARIα. Methods: We used a transgenic mouse model in which PKARIα was made resistant to oxidative activation by homozygous knock-in replacement of redox-sensitive Cysteine 17 with Serine within the regulatory subunits of PKARIα (KI). ATX-II (at 1 nmol/L) was used to acutely enhance INaL in freshly isolated ventricular myocytes from KI and wild-type (WT) control mice. Epifluorescence and confocal imaging were used to assess intracellular Ca2+ handling and ROS formation. A ruptured-patch whole-cell voltage-clamp was used to measure INaL and ICa. The impact of acutely enhanced INaL on RIα dimer formation and PKA target structures was studied using Western blot analysis. Results: ATX-II increased INaL to a similar extent in KI and WT cells, which was associated with significant cytosolic and mitochondrial ROS formation in both genotypes. Acutely activated Ca2+ handling in terms of increased Ca2+ transient amplitudes and elevated SR Ca2+ load was equally present in KI and WT cells. Likewise, cellular arrhythmias as approximated by non-triggered Ca2+ elevations during Ca2+ transient decay and by diastolic SR Ca2+-spark frequency occurred in a comparable manner in both genotypes. Most importantly and in contrast to our initial hypothesis, ATX-II did not alter the magnitude or inactivation kinetics of ICa in neither WT nor KI cells and did not result in PKARIα dimerization (i.e., oxidation) despite a clear prooxidant intracellular environment. Conclusions: The inotropic and arrhythmogenic effects of acutely increased INaL are associated with elevated ROS, but do not involve oxidation of PKARIα.

Estimation of Individual Positive Anti-Islet Autoantibodies from 3 Screen ICA Titer.

The 3 Screen ICA ELISA is a novel assay capable of simultaneously measuring autoantibodies to glutamic acid decarboxylase (GADA), insulinoma-associated antigen-2 (IA-2A), and zinc transporter 8 (ZnT8A), making it a valuable tool for screening type 1 diabetes. Despite its advantages, it cannot specify which individual autoantibodies are positive or negative. This study aimed to estimate individual positive autoantibodies based on the 3 Screen ICA titer. Six hundred seventeen patients with type 1 diabetes, simultaneously measured for 3 Screen ICA and three individual autoantibodies, were divided into five groups based on their 3 Screen ICA titer. The sensitivities and contribution rates of the individual autoantibodies were then examined. The study had a cross-sectional design. Sixty-nine percent (424 of 617) of patients with type 1 diabetes had 3 Screen ICA titers exceeding the 99th percentile cut-off level (20 index). The prevalence of GADA ranged from 80% to 100% in patients with a 3 Screen ICA over 30 index and 97% of patients with a 3 Screen ICA ≥300 index. Furthermore, the prevalence of all individual autoantibodies being positive was 0% for ≤80 index and as high as 92% for ≥300 index. Significant associations were observed in specific titer groups: the 20-29.9 index group when all the individual autoantibodies were negative, the 30-79.9 index group when positive for GADA alone or IA-2A alone, the 30-299.9 index group when positive for ZnT8A alone, the 80-299.9 index group when positive for both IA-2A and ZnT8A, the 300-499.9 index group when positive for both GADA and ZnT8A, and the ≥300 index group when positive for all individual autoantibodies. These results suggest that the 3 Screen ICA titer may be helpful in estimating individual positive autoantibodies.

Clinical Outcomes after Intracorporeal versus Extracorporeal Anastomosis in Patients Undergoing Laparoscopic Right Hemicolectomy for Colon Cancer.

Background and Objectives: Laparoscopic right hemicolectomy (LRHC) is commonly performed for patients with colon cancer, selecting between intracorporeal anastomosis (ICA) or extracorporeal anastomosis (ECA). However, the impact of ICA versus ECA on patient outcomes remains debatable. The varying levels of experience among surgeons may influence the outcomes. Therefore, this study sought to compare the short- and long-term outcomes of LRHC using ICA versus ECA. Materials and Methods: This retrospective study extracted patient data from the medical records database of Changhua Christian Hospital, Taiwan, from 2017 to 2020. Patients with colon cancer who underwent LRHC with either ICA or ECA were included. Primary outcomes were post-surgical outcomes and secondary outcomes were recurrence rate, overall survival (OS), and cancer-specific survival (CSS). Between-group differences were compared using chi-square, t-tests, and Fisher's exact tests and Mann-Whitney U tests. Associations between study variables, OS, and CSS were determined using Cox analyses. Results: Data of 240 patients (61 of ICA and 179 of ECA) with a mean age of 65.0 years and median follow-up of 49.3 months were collected. No recognized difference was found in patient characteristics between these two groups. The ICA group had a significantly shorter operation duration (median (IQR): 120 (110-155) vs. 150 (130-180) min) and less blood loss (median (IQR): 30 (10-30) vs. 30 (30-50) mL) than the ECA group (p < 0.001). No significant differences were found in 30-day readmission (ICA vs. ECA: 1.6% vs. 2.2%, p > 0.999) or recurrence (18.0% vs. 13.4%, p = 0.377) between the two groups. Multivariable analyses revealed no significant differences in OS (adjusted hazard ratio (aHR): 0.65; 95% confidence interval (CI): 0.25-1.44) or CSS (adjusted sub-hazard ratio (aSHR): 0.41, 95% CI: 0.10-1.66) between groups. Conclusions: Both ICA and ECA in LRHC for colon cancer had similar outcomes without statistically significant differences in post-surgical complications, 30-day readmission rates, recurrence rate, and long-term survival outcomes. However, ICA may offer two advantages in terms of a shorter operative duration and reduced blood loss.

Passive hyperthermia alters the resting-state functional connectivity of mouse brain.

PURPOSE: To investigate how passive hyperthermia affect the resting-state functional brain activity based on an acute mouse model after heat stress exposure. MATERIALS AND METHODS: Twenty-eight rs-fMRI data of C57BL/6J male mice which weighing about 24 ∼ 29 g and aged 12 ∼ 16 weeks were collected. The mice in the hyperthermia group (HT, 40 °C ± 0.5 °C, 40 min) were subjected to passive hyperthermia before the anesthesia preparation for scanning. While the normal control group (NC) was subjected to normothermia condition (NC, 20 °C ± 2 °C, 40 min). After data preprocessing, we performed independent component analysis (ICA) and region of interested (ROI)-ROI functional connectivity (FC) analyses on the data of both HT (n = 13) and NC (n = 15). RESULTS: The group ICA analysis showed that the HT and the NC both included 11 intrinsic connectivity networks (ICNs), and can be divided into four types of networks: the cortical network (CN), the subcortical network (SN), the default mode network (DMN), and cerebellar networks. CN and SN belongs to sensorimotor network. Compared with NC, the functional network organization of ICNs in the HT was altered and the overall functional intensity was decreased. Furthermore, 13 ROIs were selected in CN, SN, and DMN for further ROI-ROI FC analysis. The ROI-ROI FC analysis showed that passive hyperthermia exposure significantly reduced the FC strength in the overall brain represented by CN, SN, DMN of mice. CONCLUSION: Prolonged exposure to high temperature has a greater impact on the overall perception and cognitive level of mice, which might help understand the relationship between neuronal activities and physiological thermal sensation and regulation as well as behavioral changes.

RNAseq of INOCA patients identifies innate, invariant, and acquired immune changes: potential autoimmune microvascular dysfunction.

Background: Ischemia with non-obstructive coronary arteries (INOCA) is a major clinical entity that involves potentially 20%-30% of patients with chest pain. INOCA is typically attributed either to coronary microvascular disease and/or vasospasm, but is likely distinct from classical coronary artery disease (CAD). Objectives: To gain insights into the etiology of INOCA and CAD, RNA sequencing of whole blood from patients undergoing both stress testing and elective invasive coronary angiography (ICA) was conducted. Methods: Stress testing and ICA of 177 patients identified 40 patients (23%) with INOCA compared to 39 controls (stress-, ICA-). ICA+ patients divided into 38 stress- and 60 stress+. RNAseq was performed by Illumina with ribosomal RNA depletion. Transcriptome changes were analyzed by DeSeq2 and curated by manual and automated methods. Results: Differentially expressed genes for INOCA were associated with elevated levels of transcripts related to mucosal-associated invariant T (MAIT) cells, plasmacytoid dendritic cells (pcDC), and memory B cells, and were associated with autoimmune diseases such as rheumatoid arthritis. Decreased transcripts were associated with neutrophils, but neutrophil transcripts, per se, were not less abundant in INOCA. CAD transcripts were more related to T cell functions. Conclusions: Elevated transcripts related to pcDC, MAIT, and memory B cells suggest an autoimmune component to INOCA. Reduced neutrophil transcripts are likely attributed to chronic activation leading to increased translation and degradation. Thus, INOCA could result from stimulation of B cell, pcDC, invariant T cell, and neutrophil activation that compromises cardiac microvascular function.

Linking functional and structural brain organisation with behaviour in healthy adults.

Multimodal data integration approaches, such as Linked Independent Component Analysis (LICA), increase sensitivity to brain-behaviour relationships and allow us to probe the relationship between modalities. Here we focus on inter-regional functional and structural organisation to determine if organisational patterns persist across modalities and if investigating multi-modality organisations provides increased sensitivity to brain-behaviour associations. We utilised multimodal magnetic resonance imaging (MRI; T1w, resting-state functional [fMRI] and diffusion weighted [DWI]) and behavioural data from the Human Connectome Project (HCP, n=676; 51% female). Unimodal features were extracted to produce individual grey matter density maps, probabilistic tractography connectivity matrices and connectopic maps from the T1w, DWI and fMRI data, respectively. DWI and fMRI analyses were restricted to subcortical regions for computational reasons. LICA was then used to integrate features, generating 100 novel independent components. Associations between these components and demographic/behavioural (n=308) variables were examined. 15 components were significantly associated with various demographic/behavioural measures. 2 components were strongly related to various measures of intoxication, driven by DWI and resemble components previously identified. Another component was driven by striatal functional data and related to working memory. A small number of components showed shared variance between structure and function but none of these displayed any significant behavioural associations. Our working memory findings provide support for the use of fMRI connectopic mapping in future research of working memory. Given the lack of behaviourally relevant shared variance between functional and structural organisation, as indexed here, we question the utility of integrating connectopic maps and tractography data.

Towards interpretable Cryo-EM: disentangling latent spaces of molecular conformations.

Molecules are essential building blocks of life and their different conformations (i.e., shapes) crucially determine the functional role that they play in living organisms. Cryogenic Electron Microscopy (cryo-EM) allows for acquisition of large image datasets of individual molecules. Recent advances in computational cryo-EM have made it possible to learn latent variable models of conformation landscapes. However, interpreting these latent spaces remains a challenge as their individual dimensions are often arbitrary. The key message of our work is that this interpretation challenge can be viewed as an Independent Component Analysis (ICA) problem where we seek models that have the property of identifiability. That means, they have an essentially unique solution, representing a conformational latent space that separates the different degrees of freedom a molecule is equipped with in nature. Thus, we aim to advance the computational field of cryo-EM beyond visualizations as we connect it with the theoretical framework of (nonlinear) ICA and discuss the need for identifiable models, improved metrics, and benchmarks. Moving forward, we propose future directions for enhancing the disentanglement of latent spaces in cryo-EM, refining evaluation metrics and exploring techniques that leverage physics-based decoders of biomolecular systems. Moreover, we discuss how future technological developments in time-resolved single particle imaging may enable the application of nonlinear ICA models that can discover the true conformation changes of molecules in nature. The pursuit of interpretable conformational latent spaces will empower researchers to unravel complex biological processes and facilitate targeted interventions. This has significant implications for drug discovery and structural biology more broadly. More generally, latent variable models are deployed widely across many scientific disciplines. Thus, the argument we present in this work has much broader applications in AI for science if we want to move from impressive nonlinear neural network models to mathematically grounded methods that can help us learn something new about nature.

Adipose-derived stem cell exosomes loaded with icariin alleviates rheumatoid arthritis by modulating macrophage polarization in rats.

Rheumatoid arthritis (RA) is a chronic autoimmune disease marked by synovitis and cartilage destruction. The active compound, icariin (ICA), derived from the herb Epimedium, exhibits potent anti-inflammatory properties. However, its clinical utility is limited by its water insolubility, poor permeability, and low bioavailability. To address these challenges, we developed a multifunctional drug delivery system-adipose-derived stem cells-exosomes (ADSCs-EXO)-ICA to target active macrophages in synovial tissue and modulate macrophage polarization from M1 to M2. High-performance liquid chromatography analysis confirmed a 92.4 ± 0.008% loading efficiency for ADSCs-EXO-ICA. In vitro studies utilizing cellular immunofluorescence (IF) and flow cytometry demonstrated significant inhibition of M1 macrophage proliferation by ADSCs-EXO-ICA. Enzyme-linked immunosorbent assay, cellular transcriptomics, and real-time quantitative PCR indicated that ADSCs-EXO-ICA promotes an M1-to-M2 phenotypic transition by reducing glycolysis through the inhibition of the ERK/HIF-1α/GLUT1 pathway. In vivo, ADSCs-EXO-ICA effectively accumulated in the joints. Pharmacodynamic assessments revealed that ADSCs-EXO-ICA decreased cytokine levels and mitigated arthritis symptoms in collagen-induced arthritis (CIA) rats. Histological analysis and micro computed tomography confirmed that ADSCs-EXO-ICA markedly ameliorated synovitis and preserved cartilage. Further in vivo studies indicated that ADSCs-EXO-ICA suppresses arthritis by promoting an M1-to-M2 switch and suppressing glycolysis. Western blotting supported the therapeutic efficacy of ADSCs-EXO-ICA in RA, confirming its role in modulating macrophage function through energy metabolism regulation. Thus, this study not only introduces a drug delivery system that significantly enhances the anti-RA efficacy of ADSCs-EXO-ICA but also elucidates its mechanism of action in macrophage function inhibition.

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.

Deep learning based diagnosis of PTSD using 3D-CNN and resting-state fMRI data.

BACKGROUND: The incidence rate of Posttraumatic stress disorder (PTSD) is currently increasing due to wars, terrorism, and pandemic disease situations. Therefore, accurate detection of PTSD is crucial for the treatment of the patients, for this purpose, the present study aims to classify individuals with PTSD versus healthy control. METHODS: The resting-state functional MRI (rs-fMRI) scans of 19 PTSD and 24 healthy control male subjects have been used to identify the activation pattern in most affected brain regions using group-level independent component analysis (ICA) and t-test. To classify PTSD-affected subjects from healthy control six machine learning techniques including random forest, Naive Bayes, support vector machine, decision tree, K-nearest neighbor, linear discriminant analysis, and deep learning three-dimensional 3D-CNN have been performed on the data and compared. RESULTS: The rs-fMRI scans of the most commonly investigated 11 regions of trauma-exposed and healthy brains are analyzed to observe their level of activation. Amygdala and insula regions are determined as the most activated regions from the regions-of-interest in the brain of PTSD subjects. In addition, machine learning techniques have been applied to the components extracted from ICA but the models provided low classification accuracy. The ICA components are also fed into the 3D-CNN model, which is trained with a 5-fold cross-validation method. The 3D-CNN model demonstrated high accuracies, such as 98.12%, 98.25 %, and 98.00 % on average with training, validation, and testing datasets, respectively. CONCLUSION: The findings indicate that 3D-CNN is a surpassing method than the other six considered techniques and it helps to recognize PTSD patients accurately.

ICA1 affects APP processing through the PICK1-PKCα signaling pathway.

AIMS: Islet cell autoantigen 1 (ICA1) is involved in autoimmune diseases and may affect synaptic plasticity as a neurotransmitter. Databases related to Alzheimer's disease (AD) have shown decreased ICA1 expression in patients with AD. However, the role of ICA1 in AD remains unclear. Here, we report that ICA1 expression is decreased in the brains of patients with AD and an AD mouse model. RESULTS: The ICA1 increased the expression of amyloid precursor protein (APP), disintegrin and metalloprotease 10 (ADAM10), and disintegrin and metalloprotease 17 (ADAM17), but did not affect protein half-life or mRNA levels. Transcriptome sequencing analysis showed that ICA1 regulates the G protein-coupled receptor signaling pathway. The overexpression of ICA1 increased PKCα protein levels and phosphorylation. CONCLUSION: Our results demonstrated that ICA1 shifts APP processing to non-amyloid pathways by regulating the PICK1-PKCα signaling pathway. Thus, this study suggests that ICA1 is a novel target for the treatment of AD.

Group ICA of wide-field calcium imaging data reveals the retrosplenial cortex as a major contributor to cortical activity during anesthesia.

Large-scale cortical dynamics play a crucial role in many cognitive functions such as goal-directed behaviors, motor learning and sensory processing. It is well established that brain states including wakefulness, sleep, and anesthesia modulate neuronal firing and synchronization both within and across different brain regions. However, how the brain state affects cortical activity at the mesoscale level is less understood. This work aimed to identify the cortical regions engaged in different brain states. To this end, we employed group ICA (Independent Component Analysis) to wide-field imaging recordings of cortical activity in mice during different anesthesia levels and the awake state. Thanks to this approach we identified independent components (ICs) representing elements of the cortical networks that are common across subjects under decreasing levels of anesthesia toward the awake state. We found that ICs related to the retrosplenial cortices exhibited a pronounced dependence on brain state, being most prevalent in deeper anesthesia levels and diminishing during the transition to the awake state. Analyzing the occurrence of the ICs we found that activity in deeper anesthesia states was characterized by a strong correlation between the retrosplenial components and this correlation decreases when transitioning toward wakefulness. Overall these results indicate that during deeper anesthesia states coactivation of the posterior-medial cortices is predominant over other connectivity patterns, whereas a richer repertoire of dynamics is expressed in lighter anesthesia levels and the awake state.

Aberrant Intra- and Inter-Network Connectivity in Idiopathic Sudden Sensorineural Hearing Loss with Tinnitus.

BACKGROUND: Idiopathic Sudden Sensorineural Hearing Loss (ISSNHL) is related to alterations in brain cortical and subcortical structures, and changes in brain functional activities involving multiple networks, which is often accompanied by tinnitus. There have been many in-depth research studies conducted concerning ISSNHL. Despite this, the neurophysiological mechanisms of ISSNHL with tinnitus are still under exploration. OBJECTIVE: The study aimed to investigate the neural mechanism in ISSNHL patients with tinnitus based on the alterations in intra- and inter-network Functional Connectivity (FC) of multiple networks. METHODS: Thirty ISSNHL subjects and 37 healthy subjects underwent resting-state functional Magnetic Resonance Imaging (rs-fMRI). Independent Component Analysis (ICA) was used to identify 8 Resting-state Networks (RSNs). Furthermore, the study used a two-sample t-test to calculate the intra-network FC differences, while calculating Functional Network Connectivity (FNC) to detect the inter-network FC differences. RESULTS: By using the ICA approach, tinnitus patients with ISSNHL were found to have FC changes in the following RSNs: CN, VN, DMN, ECN, SMN, and AUN. In addition, the interconnections of VN-SMN, VN-ECN, and ECN-DAN were weakened. CONCLUSION: The present study has demonstrated changes in FC within and between networks in ISSNHL with tinnitus, providing ideas for further study on the neuropathological mechanism of the disease.

Imaging of cerebrovascular complications from blunt skull base trauma.

Cerebrovascular complications from blunt trauma to the skull base, though rare, can lead to potentially devastating outcomes, emphasizing the importance of timely diagnosis and management. Due to the insidious clinical presentation, subtle nature of imaging findings, and complex anatomy of the skull base, diagnosing cerebrovascular injuries and their complications poses considerable challenges. This article offers a comprehensive review of skull base anatomy and pathophysiology pertinent to recognizing cerebrovascular injuries and their complications, up-to-date screening criteria and imaging techniques for assessing these injuries, and a case-based review of the spectrum of cerebrovascular complications arising from skull base trauma. This review will enhance understanding of cerebrovascular injuries and their complications from blunt skull base trauma to facilitate diagnosis and timely treatment.

Novel Feature Generation for Classification of Motor Activity from Functional Near-Infrared Spectroscopy Signals Using Machine Learning.

Recent research in the field of cognitive motor action decoding focuses on data acquired from Functional Near-Infrared Spectroscopy (fNIRS) and its analysis. This research aims to classify two different motor activities, namely, mental drawing (MD) and spatial navigation (SN), using fNIRS data from non-motor baseline data and other motor activities. Accurate activity detection in non-stationary signals like fNIRS is challenging and requires complex feature descriptors. As a novel framework, a new feature generation by fusion of wavelet feature, Hilbert, symlet, and Hjorth parameters is proposed for improving the accuracy of the classification. This new fused feature has statistical descriptor elements, time-localization in the frequency domain, edge feature, texture features, and phase information to detect and locate the activity accurately. Three types of independent component analysis, including FastICA, Picard, and Infomax were implemented for preprocessing which removes noises and motion artifacts. Two independent binary classifiers are designed to handle the complexity of classification in which one is responsible for mental drawing (MD) detection and the other one is spatial navigation (SN). Four different types of algorithms including nearest neighbors (KNN), Linear Discriminant Analysis (LDA), light gradient-boosting machine (LGBM), and Extreme Gradient Boosting (XGBOOST) were implemented. It has been identified that the LGBM classifier gives high accuracies-98% for mental drawing and 97% for spatial navigation. Comparison with existing research proves that the proposed method gives the highest classification accuracies. Statistical validation of the proposed new feature generation by the Kruskal-Wallis H-test and Mann-Whitney U non-parametric test proves the reliability of the proposed mechanism.

A method for estimating dynamic functional network connectivity gradients (dFNG) from ICA captures smooth inter-network modulation.

Dynamic functional network connectivity (dFNC) analysis is a widely used approach for studying brain function and offering insight into how brain networks evolve over time. Typically, dFNC studies utilized fixed spatial maps and evaluate transient changes in coupling among time courses estimated from independent component analysis (ICA). This manuscript presents a complementary approach that relaxes this assumption by spatially reordering the components dynamically at each timepoint to optimize for a smooth gradient in the FNC (i.e., a smooth gradient among ICA connectivity values). Several methods are presented to summarize dynamic FNC gradients (dFNGs) over time, starting with static FNC gradients (sFNGs), then exploring the reordering properties as well as the dynamics of the gradients themselves. We then apply this approach to a dataset of schizophrenia (SZ) patients and healthy controls (HC). Functional dysconnectivity between different brain regions has been reported in schizophrenia, yet the neural mechanisms behind it remain elusive. Using resting state fMRI and ICA on a dataset consisting of 151 schizophrenia patients and 160 age and gender-matched healthy controls, we extracted 53 intrinsic connectivity networks (ICNs) for each subject using a fully automated spatially constrained ICA approach. We develop several summaries of our functional network connectivity gradient analysis, both in a static sense, computed as the Pearson correlation coefficient between full time series, and a dynamic sense, computed using a sliding window approach followed by reordering based on the computed gradient, and evaluate group differences. Static connectivity analysis revealed significantly stronger connectivity between subcortical (SC), auditory (AUD) and visual (VIS) networks in patients, as well as hypoconnectivity in sensorimotor (SM) network relative to controls. sFNG analysis highlighted distinctive clustering patterns in patients and HCs along cognitive control (CC)/ default mode network (DMN), as well as SC/ AUD/ SM/ cerebellar (CB), and VIS gradients. Furthermore, we observed significant differences in the sFNGs between groups in SC and CB domains. dFNG analysis suggested that SZ patients spend significantly more time in a SC/ CB state based on the first gradient, while HCs favor the SM/DMN state. For the second gradient, however, patients exhibited significantly higher activity in CB domains, contrasting with HCs' DMN engagement. The gradient synchrony analysis conveyed more shifts between SM/ SC networks and transmodal CC/ DMN networks in patients. In addition, the dFNG coupling revealed distinct connectivity patterns between SC, SM and CB domains in SZ patients compared to HCs. To recap, our results advance our understanding of brain network modulation by examining smooth connectivity trajectories. This provides a more complete spatiotemporal summary of the data, contributing to the growing body of current literature regarding the functional dysconnectivity in schizophrenia patients. By employing dFNG, we highlight a new perspective to capture large scale fluctuations across the brain while maintaining the convenience of brain networks and low dimensional summary measures.

Stereotypical Hippocampal Clustering Predicts Navigational Success in Virtualized Real-World Environments.

Structural differences along the hippocampal long axis are believed to underlie meaningful functional differences. Yet, recent data-driven parcellations of the hippocampus subdivide the hippocampus into a 10-cluster map with anterior-medial, anterior-lateral, and posteroanterior-lateral, middle, and posterior components. We tested whether task and experience could modulate this clustering using a spatial learning experiment where male and female participants were trained to virtually navigate a novel neighborhood in a Google Street View-like environment. Participants were scanned while navigating routes early in training and after a 2 week training period. Using the 10-cluster map as the ideal template, we found that participants who eventually learn the neighborhood well have hippocampal cluster maps consistent with the ideal-even on their second day of learning-and their cluster mappings do not deviate over the 2 week training period. However, participants who eventually learn the neighborhood poorly begin with hippocampal cluster maps inconsistent with the ideal template, though their cluster mappings may become more stereotypical after the 2 week training. Interestingly this improvement seems to be route specific: after some early improvement, when a new route is navigated, participants' hippocampal maps revert back to less stereotypical organization. We conclude that hippocampal clustering is not dependent solely on anatomical structure and instead is driven by a combination of anatomy, task, and, importantly, experience. Nonetheless, while hippocampal clustering can change with experience, efficient navigation depends on functional hippocampal activity clustering in a stereotypical manner, highlighting optimal divisions of processing along the hippocampal anterior-posterior and medial-lateral axes.

Narcissus reflected: Grey and white matter features joint contribution to the default mode network in predicting narcissistic personality traits.

Despite the clinical significance of narcissistic personality, its neural bases have not been clarified yet, primarily because of methodological limitations of the previous studies, such as the low sample size, the use of univariate techniques and the focus on only one brain modality. In this study, we employed for the first time a combination of unsupervised and supervised machine learning methods, to identify the joint contributions of grey matter (GM) and white matter (WM) to narcissistic personality traits (NPT). After preprocessing, the brain scans of 135 participants were decomposed into eight independent networks of covarying GM and WM via parallel ICA. Subsequently, stepwise regression and Random Forest were used to predict NPT. We hypothesized that a fronto-temporo parietal network, mainly related to the default mode network, may be involved in NPT and associated WM regions. Results demonstrated a distributed network that included GM alterations in fronto-temporal regions, the insula and the cingulate cortex, along with WM alterations in cerebellar and thalamic regions. To assess the specificity of our findings, we also examined whether the brain network predicting narcissism could also predict other personality traits (i.e., histrionic, paranoid and avoidant personalities). Notably, this network did not predict such personality traits. Additionally, a supervised machine learning model (Random Forest) was used to extract a predictive model for generalization to new cases. Results confirmed that the same network could predict new cases. These findings hold promise for advancing our understanding of personality traits and potentially uncovering brain biomarkers associated with narcissism.

Parallel Multilink Group Joint ICA: Fusion of 3D Structural and 4D Functional Data Across Multiple Resting fMRI Networks.

Multimodal neuroimaging research plays a pivotal role in understanding the complexities of the human brain and its disorders. Independent component analysis (ICA) has emerged as a widely used and powerful tool for disentangling mixed independent sources, particularly in the analysis of functional magnetic resonance imaging (fMRI) data. This paper extends the use of ICA as a unifying framework for multimodal fusion, introducing a novel approach termed parallel multilink group joint ICA (pmg-jICA). The method allows for the fusion of gray matter maps from structural MRI (sMRI) data to multiple fMRI intrinsic networks, addressing the limitations of previous models. The effectiveness of pmg-jICA is demonstrated through its application to an Alzheimer's dataset, yielding linked structure-function outputs for 53 brain networks. Our approach leverages the complementary information from various imaging modalities, providing a unique perspective on brain alterations in Alzheimer's disease. The pmg-jICA identifies several components with significant differences between HC and AD groups including thalamus, caudate, putamen with in the subcortical (SC) domain, insula, parahippocampal gyrus within the cognitive control (CC) domain, and the lingual gyrus within the visual (VS) domain, providing localized insights into the links between AD and specific brain regions. In addition, because we link across multiple brain networks, we can also compute functional network connectivity (FNC) from spatial maps and subject loadings, providing a detailed exploration of the relationships between different brain regions and allowing us to visualize spatial patterns and loading parameters in sMRI along with intrinsic networks and FNC from the fMRI data. In essence, developed approach combines concepts from joint ICA and group ICA to provide a rich set of output characterizing data-driven links between covarying gray matter networks, and a (potentially large number of) resting fMRI networks allowing further study in the context of structure/function links. We demonstrate the utility of the approach by highlighting key structure/function disruptions in Alzheimer's individuals.