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Preliminary results showed that both behavioral and brain responses across the patient-clinician dyad were significantly affected by the interaction style.Clinical Relevance- The context of a clinical intervention can significantly impact the treatment of chronic pain. Effective therapeutic alliance, based on empathy, mutual trust, and warmth can improve treatment adherence and clinical outcomes. A deeper scientific understanding of the brain and behavioral mechanisms underlying an optimal patient-clinician interaction may lead to improved quality of clinical care and physician training, as well as better understanding of the social aspects of the biopsychosocial model mediating analgesia in chronic pain patients.Pain is a subjective experience and clinicians need to treat patients with accurate pain levels. EEG has emerged as a useful tool for objective pain assessment, but due to the low signal-to-noise ratio of pain-related EEG signals, the prediction accuracy of EEG-based pain prediction models is still unsatisfactory. In this paper, we proposed an autoencoder model based on convolutional neural networks for feature extraction of pain-related EEG signals. More precisely, we used EEGNet to build an autoencoder model to extract a small set of features from high-density pain-evoked EEG potentials and then establish a machine learning models to predict pain levels (high pain vs. low pain) from extracted features. Experimental results show that the new autoencoder-based approach can effectively identify pain-related features and can achieve better classification results than conventional methods.At present, only professional doctors can use the professional scales to diagnose depression and anxiety in clinical practice. In recent years, the problems of detecting the presence of anxiety or depression using Electroencephalography (EEG) has received attention as a way to implement assistant diagnosis, and some researchers explored that there are differences in the degree of prefrontal lateralization and functional connectivity of brain networks between patients with anxiety and depression and normal people. In this paper, we proposed a new approach that combines functional connectivity of brain networks and convolutional neural networks (CNN) for EEG-based anxiety and depression recognition. EEG data are collected from subjects consisting ten healthy controls and ten patients with anxiety or depression. In this way, we achieved 67.67% classification accuracy. It points out the way to further explore the application of functional connectivity of brain networks and deep learning technology in EEG about patients with anxiety and depression.Functional magnetic resonance imaging (fMRI) is one of the most popular methods for studying the human brain. It measures brain activity, by detecting local changes of Blood Oxygen Level Dependent (BOLD) signal in the brain, over time, and can be used in both task-related and resting-state studies. In task-related studies, our aim is to determine which brain areas are activated when a specific task is performed. Various unsupervised multivariate statistical methods are being increasingly employed in fMRI data analysis. Their main goal is to extract information from a dataset, often with no prior knowledge of the experimental conditions. Generalized canonical correlation analysis (gCCA) is a well known statistical method that can be considered as a way to estimate a linear subspace, which is "common" to multiple random linear subspaces. We propose a new fMRI data generating model which takes into consideration the existence of common task-related and resting-state components. We estimate the common spatial task-related component via a two-stage gCCA. We test our theoretical results using real-world fMRI data. Our experimental findings corroborate our theoretical results, rendering our approach a very good candidate for multi-subject task-related fMRI processing.Clinical Relevance-This work provides a set of methods for amplifying and recovering commonalities across subjects that appear in data from multi-subject task-related fMRI experiments.Major depressive disorder (MDD) is a common and serious mental disorder characterized by a persistent negative feeling and tremendous sadness. In recent decades, several studies used functional network connectivity (FNC), estimated from resting state functional magnetic resonance imaging (fMRI), to investigate the biological signature of MDD. However, the majority of them have ignored the temporal change of brain interaction by focusing on static FNC (sFNC). selleck inhibitor Dynamic functional network connectivity (dFNC) that explores temporal patterns of functional connectivity (FC) might provide additional information to its static counterpart. In the current study, by applying k-means clustering on dFNC of MDD and healthy subjects (HCs), we estimated 5 different states. Next, we use the hidden Markov model as a potential biomarker to differentiate the dFNC pattern of MDD patients from HCs. Comparing MDD and HC subjects' hidden Markov model (HMM) features, we have highlighted the role of transition probabilities between states as potential biomarkers and identified that transition probability from a lightly- connected state to highly connected one reduces as symptom severity increases in MDD subjects.Index Terms- Major depressive disorder, Dynamic functional network connectivity, Machine learning, Resting- state functional magnetic resonance imaging, Hidden Markov model.Cerebrovascular Reactivity (CVR), the responsiveness of blood vessels to a vasodilatory stimulus, is an important indicator of cerebrovascular health. Assessing CVR with fMRI, we can measure the change in the Blood Oxygen Level Dependent (BOLD) response induced by a change in CO2 pressure (%BOLD/mmHg). However, there exists a temporal offset between the recorded CO2 pressure and the local BOLD response, due to both measurement and physiological delays. If this offset is not corrected for, voxel-wise CVR values will not be accurate. In this paper, we propose a framework for mapping hemodynamic lag in breath-hold fMRI data. As breath-hold tasks drive task-correlated head motion artifacts in BOLD fMRI data, our framework for lag estimation fits a model that includes polynomial terms and head motion parameters, as well as a shifted variant of the CO2 regressor (±9 s in 0.3 s increments), and the hemodynamic lag at each voxel is the shift producing the maximum total model R2 within physiological constraints. This approach is evaluated in 8 subjects with multi-echo fMRI data, resulting in robust maps of hemodynamic delay that show consistent regional variation across subjects, and improved contrast-to-noise compared to methods where motion regression is ignored or performed earlier in preprocessing.