UNASSIGNED: Patients with anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis often experience severe symptoms. Resting-state functional MRI (rs-fMRI) has revealed widespread impairment of functional networks in patients. However, the changes in information flow remain unclear. This study aims to investigate the intrinsic functional connectivity (FC) both within and between resting-state networks (RSNs), as well as the alterations in effective connectivity (EC) between these networks.
UNASSIGNED: Resting-state functional MRI (rs-fMRI) data were collected from 25 patients with anti-NMDAR encephalitis and 30 healthy controls (HCs) matched for age, sex, and educational level. Changes in the intrinsic functional connectivity (FC) within and between RSNs were analyzed using independent component analysis (ICA). The functional interaction between RSNs was identified by granger causality analysis (GCA).
UNASSIGNED: Compared to HCs, patients with anti-NMDAR encephalitis exhibited lower performance on the Wisconsin Card Sorting Test (WCST), both in terms of correct numbers and correct categories. Additionally, these patients demonstrated decreased scores on the Montreal Cognitive Assessment (MoCA). Neuroimaging studies revealed abnormal intra-FC within the default mode network (DMN), increased intra-FC within the visual network (VN) and dorsal attention network (DAN), as well as increased inter-FC between VN and the frontoparietal network (FPN). Furthermore, aberrant effective connectivity (EC) was observed among the DMN, DAN, FPN, VN, and somatomotor network (SMN).
UNASSIGNED: Patients with anti-NMDAR encephalitis displayed noticeable deficits in both memory and executive function. Notably, these patients exhibited widespread impairments in intra-FC, inter-FC, and EC. These results may help to explain the pathophysiological mechanism of anti-NMDAR encephalitis.

UNASSIGNED: We aimed to evaluate the effect of Lysergic acid diethylamide (LSD) on the pain neural network (PNN) in healthy subjects using functional magnetic resonance imaging (fMRI).
UNASSIGNED: Twenty healthy volunteers participated in a balanced-order crossover study, receiving intravenous administration of LSD and placebo in two fMRI scanning sessions. Brain regions associated with pain processing were analyzed by amplitude of low-frequency fluctuation (ALFF), independent component analysis (ICA), functional connectivity and dynamic casual modeling (DCM).
UNASSIGNED: ALFF analysis demonstrated that LSD effectively relieves pain due to modulation in the neural network associated with pain processing. ICA analysis showed more active voxels in anterior cingulate cortex (ACC), thalamus (THL)-left, THL-right, insula cortex (IC)-right, parietal operculum (PO)-left, PO-right and frontal pole (FP)-right in the placebo session than the LSD session. There were more active voxels in FP-left and IC-left in the LSD session compared to the placebo session. Functional brain connectivity was observed between THL-left and PO-right and between PO-left with FP-left, FP-right and IC-left in the placebo session. In the LSD session, functional connectivity of PO-left with FP-left and FP-right was observed. The effective connectivity between left anterior insula cortex (lAIC)-lAIC, lAIC-dorsolateral prefrontal cortex (dlPFC) and secondary somatosensory cortex (SII)-dlPFC were significantly different. Finally, the correlation between fMRI biomarkers and clinical pain criteria was calculated.
UNASSIGNED: This study enhances our understanding of the LSD effect on the architecture and neural behavior of pain in healthy subjects and provides great promise for future research in the field of cognitive science and pharmacology.

UNASSIGNED: The reliability of interpretation of coronary angiography as a diagnostic tool was investigated. Furthermore, the impact of interobserver variability of coronary lesions on clinical decision-making was assessed. One of our motivations to do this research was the research gaps and our aim to have up-to-date information regarding interobserver variability among different cardiologists.
UNASSIGNED: Our objective was to quantify interobserver variability among cardiologists who have seen angiograms independently. Disagreement among cardiologists in the visual assessment of invasive coronary angiography of coronary artery stenosis is not uncommon in previous studies. Three cardiologists with extensive experience in coronary angiography, including the primary cardiologist of each patient, read the angiograms of 200 patients from Toronto General Hospital independently.
UNASSIGNED: Our research showed the mean agreement among all participating observers was 77.4%; therefore, the interobserver variability of coronary angiography interpretation was 22.6%.
UNASSIGNED: Coronary angiography is still the gold-standard technique for guidance regarding coronary lesions. Sometimes, coronary angiography results in underestimation or overestimation of a lesion\'s functional severity. Interobserver variability should also be considered when interpreting the severity of coronary stenoses via invasive coronary angiography. This research shows that interobserver variability regarding coronary angiograms is still present (22.6%).
Plain language summary: The gold-standard method for diagnosing coronary stenosis, invasive coronary angiography has some challenges too. One of these challenges has been the difference among various cardiologists regarding determination of severity of each coronary stenosis. In this study, we focused on differences in interobserver variability in coronary angiography interpretation. Three cardiologists who were experienced in coronary angiography read each patient’s coronary angiogram separately. Overall, 200 patients with a history of angiography at Toronto General Hospital were selected randomly. The research showed that overall agreement among all participating cardiologists with regard to the reading of coronary angiograms was 77.4%. In other words, interobserver variability of 22.6% was seen among the readers.

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.

UNASSIGNED: To investigate how passive hyperthermia affect the resting-state functional brain activity based on an acute mouse model after heat stress exposure.
UNASSIGNED: 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).
UNASSIGNED: 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.
UNASSIGNED: 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.

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.
CONCLUSIONS: The findings indicate that 3D-CNN is a surpassing method than the other six considered techniques and it helps to recognize PTSD patients accurately.

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.

BACKGROUND: Complete mesocolic excision (CME) and central vascular detachment are very important procedures in surgery for colorectal cancer. Preoperative and intraoperative assessments of the anatomy of major colorectal vessels are necessary to avoid massive bleeding, especially in endoscopic surgery. A case with a rare anomaly in which the middle colic artery (MCA) and ileocolic artery (ICA) had a common trunk is reported.
METHODS: The patient was a 73-year-old woman diagnosed with ascending colon cancer on colonoscopy. Preoperative abdominal contrast-enhanced computed tomography confirmed that the MCA and ICA had a common trunk. She underwent laparoscopic ileocecal resection for the ascending colon cancer with D3 lymph node dissection. Intraoperative indocyanine green fluorescence imaging was conducted. After confirming vessel bifurcation, the ICA was dissected at the distal end of the MCA bifurcation. The patient has been followed as an outpatient, with no signs of recurrence as of 2 years postoperatively.
CONCLUSIONS: A case of an ascending colon cancer with a unique vascular bifurcation pattern was presented. Preoperative and intraoperative evaluations of the major colorectal vessels are very important for preventing perioperative and postoperative complications.

This paper presents an advanced approach for EEG artifact removal and motor imagery classification using a combination of Four Class Iterative Filtering and Filter Bank Common Spatial Pattern Algorithm with a Modified Deep Neural Network (DNN) classifier. The research aims to enhance the accuracy and reliability of BCI systems by addressing the challenges posed by EEG artifacts and complex motor imagery tasks. The methodology begins by introducing FCIF, a novel technique for ocular artifact removal, utilizing iterative filtering and filter banks. FCIF\'s mathematical formulation allows for effective artifact mitigation, thereby improving the quality of EEG data. In tandem, the FC-FBCSP algorithm is introduced, extending the Filter Bank Common Spatial Pattern approach to handle four-class motor imagery classification. The Modified DNN classifier enhances the discriminatory power of the FC-FBCSP features, optimizing the classification process. The paper showcases a comprehensive experimental setup, featuring the utilization of BCI Competition IV Dataset 2a & 2b. Detailed preprocessing steps, including filtering and feature extraction, are presented with mathematical rigor. Results demonstrate the remarkable artifact removal capabilities of FCIF and the classification prowess of FC-FBCSP combined with the Modified DNN classifier. Comparative analysis highlights the superiority of the proposed approach over baseline methods and the method achieves the mean accuracy of 98.575%.

The Myelin Regulator Factor (MYRF) is a master regulator governing myelin formation and maintenance in the central nervous system. The conservation of MYRF across metazoans and its broad tissue expression suggest it has functions extending beyond the well-established role in myelination. Loss of MYRF results in developmental lethality in both invertebrates and vertebrates, and MYRF haploinsufficiency in humans causes MYRF-related Cardiac Urogenital Syndrome, underscoring its importance in animal development; however, these mechanisms are largely unexplored. MYRF, an unconventional transcription factor, begins embedded in the membrane and undergoes intramolecular chaperone mediated trimerization, which triggers self-cleavage, allowing its N-terminal segment with an Ig-fold DNA-binding domain to enter the nucleus for transcriptional regulation. Recent research suggests developmental regulation of cleavage, yet the mechanisms remain enigmatic. While some parts of MYRF\'s structure have been elucidated, others remain obscure, leaving questions about how these motifs are linked to its intricate processing and function.