Independent Component Analysis Research Articles

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

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

The role of functional emotion circuits in distinct dimensions of psychopathology in youth

Several mental disorders emerge during childhood or adolescence and are often characterized by socioemotional difficulties, including alterations in emotion perception. Emotional facial expressions are processed in discrete functional brain modules whose connectivity patterns encode emotion categories, but the involvement of these neural circuits in psychopathology in youth is poorly understood. This study examined the associations between activation and functional connectivity patterns in emotion circuits and psychopathology during development. We used task-based fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC, N = 1221, 8–23 years) and conducted generalized psycho-physiological interaction (gPPI) analyses. Measures of psychopathology were derived from an independent component analysis of questionnaire data. The results showed positive associations between identifying fearful, sad, and angry faces and depressive symptoms, and a negative relationship between sadness recognition and positive psychosis symptoms. We found a positive main effect of depressive symptoms on BOLD activation in regions overlapping with the default mode network, while individuals reporting higher levels of norm-violating behavior exhibited emotion-specific lower functional connectivity within regions of the salience network and between modules that overlapped with the salience and default mode network. Our findings illustrate the relevance of functional connectivity patterns underlying emotion processing for behavioral problems in children and adolescents.

Disrupted Dynamic Network Attribution Associated with Gait Disorder in Cerebral Small Vessel Disease.

Background and Aims: Previous research has focused on static functional connectivity in gait disorders caused by cerebral small vessel disease (CSVD), neglecting dynamic functional connections and network attribution. This study aims to investigate alterations in dynamic functional network connectivity (dFNC) and topological organization variance in CSVD-related gait disorders. Methods: A total of 85 patients with CSVD, including 41 patients with CSVD and gait disorders (CSVD-GD), 44 patients with CSVD and non-gait disorders (CSVD-NGD), and 32 healthy controls (HC), were enrolled in this study. Five networks composed of 10 independent components were selected using independent component analysis. Sliding time window and k-means clustering methods were used for dFNC analysis. The relationship between alterations in the dFNC properties and gait metrics was further assessed. Results: Three reproducible dFNC states were determined (State 1: sparsely connected, State 2: intermediate pattern, and State 3: strongly connected). CSVD-GD showed significantly higher fractional windows (FW) and mean dwell time (MDT) in State 1 compared with CSVD-NGD. Higher local efficiency variance was observed in the CSVD-GD group compared with HC, but no differences were found in the global efficiency comparison. Both the FW and MDT in State 1 were negatively correlated with gait speed and step length, and the relationship between MDT of State 1 and gait speed was mediated by overall cognition, information processing speed, and executive function. Conclusions: Our study uncovered abnormal dFNC indicators and variations in topological organization in CSVD-GD, offering potential early prediction indicators and freshening insights into the underlying pathogenesis of gait disturbances in CSVD.

Disaggregation Estimation Method for Load and PV Output in the Distribution System using State Estimation and Independent Component Analysis

Disaggregation Estimation Method for Load and PV Output in the Distribution System using State Estimation and Independent Component Analysis

Subband Independent Component Analysis for Coherence Enhancement.

Cortico-muscular coherence (CMC) is becoming a common technique for detection and characterization of functional coupling between the motor cortex and muscle activity. It is typically evaluated between surface electromyogram (sEMG) and electroencephalogram (EEG) signals collected synchronously during controlled movement tasks. However, the presence of noise and activities unrelated to observed motor tasks in sEMG and EEG results in low CMC levels, which often makes functional coupling difficult to detect. In this paper, we introduce Coherent Subband Independent Component Analysis (CoSICA) to enhance synchronous cortico-muscular components in mixtures captured by sEMG and EEG. The methodology relies on filter bank processing to decompose sEMG and EEG signals into frequency bands. Then, it applies independent component analysis along with a component selection algorithm for re-synthesis of sEMG and EEG designed to maximize CMC levels. We demonstrate the effectiveness of the proposed method in increasing CMC levels across different signal-to-noise ratios first using simulated data. Using neurophysiological data, we then illustrate that CoSICA processing achieves a pronounced enhancement of original CMC. Our findings suggest that the proposed technique provides an effective framework for improving coherence detection. The proposed methodologies will eventually contribute to understanding of movement control and has high potential for translation into clinical practice.

Separation of Heartbeat Waveforms of Simultaneous Two-Subjects Using Independent Component Analysis and Empirical Mode Decomposition

Separation of Heartbeat Waveforms of Simultaneous Two-Subjects Using Independent Component Analysis and Empirical Mode Decomposition

Real-Time Motor Unit Tracking from sEMG Signals with Adaptive ICA on a Parallel Ultra-Low Power Processor.

Spike extraction by blind source separation (BSS) algorithms can successfully extract physiologically meaningful information from the sEMG signal, as they are able to identify motor unit (MU) discharges involved in muscle contractions. However, BSS approaches are currently restricted to isometric contractions, limiting their applicability in real-world scenarios. We present a strategy to track MUs across different dynamic hand gestures using adaptive independent component analysis (ICA): first, a pool of MUs is identified during isometric contractions, and the decomposition parameters are stored; during dynamic gestures, the decomposition parameters are updated online in an unsupervised fashion, yielding the refined MUs; then, a Pan-Tompkins-inspired algorithm detects the spikes in each MUs; finally, the identified spikes are fed to a classifier to recognize the gesture. We validate our approach on a 4-subject, 7-gesture + rest dataset collected with our custom 16-channel dry sEMG armband, achieving an average balanced accuracy of 85.58±14.91% and macro-F1 score of 85.86±14.48%. We deploy our solution onto GAP9, a parallel ultra-low-power microcontroller specialized for computation-intensive linear algebra applications at the edge, obtaining an energy consumption of 4.72 mJ @ 240 MHz and a latency of 121.3 ms for each 200 ms-long window of sEMG signal.

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

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. 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. 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. 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.

Torture Exposure Modulates Cognitive Control and Attention Neural Network Connectivity During a Go/NoGo Task

BackgroundTorture trauma is characterized by intentional uncontrollable acts, but the long-term effects of torture exposure on cognitive control brain mechanisms are unknown. MethodsA final sample of 33 torture survivors (TSs) and 44 non-TSs, all with a refugee background, completed a Go/NoGo response inhibition task during functional magnetic resonance imaging scanning. Data-driven independent component analysis identified active networks across the task and on Go, NoGo, and error of commission trials. Groups were compared on within-/between-network connectivity while for demographic and psychological symptom covariates were controlled for. Secondary analyses investigated whether network connectivity moderated the associations between torture exposure and severity on fear (e.g., re-experiencing) and dysphoria (e.g., anhedonia) posttraumatic stress disorder symptoms. ResultsThe TS group exhibited decreased connectivity (compared with the non-TS control group) within the posterior default mode network (specifically the left precuneus) and auditory-motor network (specifically the right superior temporal gyrus) and reduced connectivity between the dorsomedial frontal network and dorsal attention network across the Go/NoGo task. The TS group also showed more negative ventral attention network connectivity during NoGo (i.e., inhibition) trials. No behavioral effects were observed. Secondary analyses revealed that the association between torture exposure and elevated posttraumatic stress disorder dysphoria (not fear) symptoms was moderated by reduced connectivity in the right superior temporal gyrus and between the dorsomedial frontal network and the dorsal attention network. ConclusionsResponse inhibition, attention, and motor networks appear to be less connected in TSs, which may be specifically linked to posttraumatic stress disorder dysphoria symptom profiles. The findings suggest that targeting cognitive control processes may hold promise for alleviating posttraumatic symptoms among survivors of torture.

Cognitive and psychiatric relevance of dynamic functional connectivity states in a large (N > 10,000) children population.

Children's brains dynamically adapt to the stimuli from the internal state and the external environment, allowing for changes in cognitive and mental behavior. In this work, we performed a large-scale analysis of dynamic functional connectivity (DFC) in children aged 9~11 years, investigating how brain dynamics relate to cognitive performance and mental health at an early age. A hybrid independent component analysis framework was applied to the Adolescent Brain Cognitive Development (ABCD) data containing 10,988 children. We combined a sliding-window approach with k-means clustering to identify five brain states with distinct DFC patterns. Interestingly, the occurrence of a strongly connected state with the most within-network synchrony and the anticorrelations between networks, especially between the sensory networks and between the cerebellum and other networks, was negatively correlated with cognitive performance and positively correlated with dimensional psychopathology in children. Meanwhile, opposite relationships were observed for a DFC state showing integration of sensory networks and antagonism between default-mode and sensorimotor networks but weak segregation of the cerebellum. The mediation analysis further showed that attention problems mediated the effect of DFC states on cognitive performance. This investigation unveils the neurological underpinnings of DFC states, which suggests that tracking the transient dynamic connectivity may help to characterize cognitive and mental problems in children and guide people to provide early intervention to buffer adverse influences.

In-Car Speech Enhancement Based on Source Separation Technique

Purpose: The purpose of this study was to investigate and analyze to increase the quality and intelligibility of speech in cars. The passenger dialogue inside the car, the sound of other equipment, and a wide range of interference effects are major challenges in the task of speech separation in-car environment.Methods: Speech enhancement based on the source separation algorithm has been proposed to enhance the preferred speech signals using a microphone array inside a car. The proposed approach determines the signal direction in the time domain by utilizing the time difference of arrival (TDOA). TDOA signals are processed, and an adaptive least mean square method is used to determine the enhanced preferred signal.Results: Experimental results show that the proposed approach yields an signal-to-noise ratio (SNR) of 7.4, perceptual evaluation of speech quality (PESQ) of 2.33 respectively. The proposed strategy outperforms existing methods in terms of PESQ, and SNR. The PESQ of the proposed method is 2.45%, and 5.22% better than the existing Independent Component Analyses, and Run length Source techniques.Conclusion: Finally, compared to the existing methods, the suggested speech enhancement algorithm is more reliable and flexible, and it can properly identify the exact location of the sound.

Diagnosis of Permanent Magnet Assisted Synchronous Reluctance Motor Winding Fault by Convolutional Neural Network

In recent years, the use of machine learning models for fault detection has become commonplace. Its goal is to identify and fix problems with permanent magnet synchronous reluctance motors. This research’s primary goal is to identify and categorize errors in their early stages. We classified winding faults using machine learning approaches, such as Independent Component Analysis and Deep Learning models. We could distinguish between vibration and current signals from the engine signals by using Independent Component Analysis (ICA). We experimented on multiple architectures using the convolutional neural network (CNN) architecture we designed from scratch and the Transfer Learning technique, testing two distinct datasets we generated using the signals we got. According to experimental findings, the suggested scratch CNN model performed exceptionally well in classification, achieving 98.6% with current signals and 99.4% with vibration signals.

Differences in gray matter atrophy and functional connectivity between motor subtypes of Parkinson's disease.

Parkinson's disease (PD) patients with postural gait abnormalities exhibit poorer motor function scores, more severe non-motor symptoms, faster cognitive function deterioration, and a less favorable response to drugs and surgery compared to PD patients with tremor. This discrepancy is believed to be associated with more pronounced gray matter atrophy and abnormal functional connectivity. To investigate the distinctive pathological mechanisms between PD subtypes, we examined gray matter volume (GMV) and functional connectivity in patients with Parkinson's disease presenting with postural instability/gait difficulty (PD-PIGD), patients with tremor-dominant Parkinson's disease (PD-TD), and healthy controls. Voxel-based morphometry (VBM) of T1-weighted images was conducted to compare GMV among 64 PD-PIGD patients, 44 PD-TD patients, and 32 controls. Subsequently, functional connectivity within regions showing reduced GMV was compared across the groups. We analyzed whether differences among the groups were associated with clinical characteristics and neuroimaging biomarkers using partial correlation and binary logistic regression. Our comparison between PD-PIGD and PD-TD patients revealed a link between PD-PIGD and more extensive frontotemporal atrophy, potentially indicating increased basal ganglia activity accompanied by decreased cerebellum activity. Furthermore, in addition to the smaller GMV in the left middle temporal gyrus, the increased functional connectivity between this brain region and the right caudate was also the independent risk factor for PD-PIGD. In addition, we compared brain network connectivity between the PIGD and TD subtypes, using an independent component analysis (ICA). We found that Compared to PD-TD, PD-PIGD patients showed an enhanced sensorimotor network (SMN) around the left supplementary motor area. These findings suggest that severe gray matter atrophy and abnormal functional connectivity and brain networks may serve as pathophysiological mechanisms distinguishing PD-PIGD patients from other subtypes.

Charging Station Power System Data Prediction Model Based on Deep Learning

The increase growth and adoption of electric vehicles (EVs) are playing a crucial role in advanced transportation system, helping to minimizing the emissions of harmful greenhouse gases and enhancing environmental sustainability. The need for EVs to be charged immediately has gained significant attention due to the rise in EVs sales over the last years. As a result of this, need for electric car charging is important to reducing the impact of electric network and providing minimum charging fares. In order to calculate the demand for charging EVs, a novel Deep Learning (DL)-based Long-Short Term Memory (LSTM) recurrent neural network predictor model is attempted to be developed in this research study. The Modified Aquilla Optimizer Algorithm (MAOA) is used to optimizing the parameter of the new Deep LSTM (DLSTM) neural predictor models and Independent Component Analysis (ICA) is utilized to solve the input time series data while conserving its properties. In this research, a novel ICA—AOA—DLSTM neural predictor model was developed to addressed the challenges of vanishing and exploding gradient in basic recurrent neural learnings. The predictor model was tested on the EV charging dataset from Georgia Tech, Atlanta, USA, indicating its performance. During simulation, the predictor achieved a prediction accuracies of 96.24% with a mean absolute errors of 0.1083 and a RMSE errors of 3.0629 × 10^-5, outperforming previous techniques. Additionally, the mean absolute error was found to be 0.2083, with a mean square error of 3.25516 × 10^-10. These results highlight the effectiveness of the novel deep learning LSTM neural predictor for this dataset compared to existing techniques.

From brain connectivity to cognitive function: Dissecting the salience network in pediatric BECTS-ESES

BackgroundBenign childhood epilepsy with centrotemporal spikes (BECTS), a common pediatric epilepsy, may lead to cognitive decline when compounded by Electrical Status Epilepticus during Sleep (ESES). Emerging evidence suggests that disruptions in the Salience Network (SN) contribute significantly to the cognitive deficits observed in BECTS-ESES. Our study rigorously investigates the dynamic functional connectivity (dFC) within the SN and its correlation with cognitive impairments in BECTS-ESES, employing advanced neuroimaging and neuropsychological assessments. MethodsIn this research, 45 patients diagnosed with BECTS-ESES and 55 age-matched healthy controls (HCs) participated. We utilized resting-state functional magnetic resonance imaging (fMRI) and Independent Component Analysis (ICA) to identify three fundamental SN nodes: the right Anterior Insula (rAI), left Anterior Insula (lAI), and the Anterior Cingulate Cortex (ACC). A two-sample t-test facilitated the comparison of dFC between these pivotal regions and other brain areas. ResultsSignificantly, the BECTS-ESES group demonstrated increased dFC, particularly between the ACC and the right Middle Occipital Gyrus, and from the rAI to the right Superior Parietal Gyrus and Cerebellum, and from the lAI to the left Postcentral Gyrus. Such dFC augmentations provide neural insights potentially explaining the neuropsychological deficits in BECTS-ESES children. Employing comprehensive neuropsychological evaluations, we mapped these dFC disruptions to specific cognitive impairments encompassing memory, executive functioning, language, and attention. Through multiple regression analysis and path analysis, a preliminary but compelling association was discovered linking dFC disturbances directly to cognitive impairments. These findings underscore the critical role of SN disruptions in BECTS-ESES cognitive dysfunctions. LimitationOur cross-sectional design and analytic methods preclude definitive mediation models and causal inferences, leaving the precise nature of dFC's mediating role and its direct impact by BECTS-ESES partially unresolved. Future longitudinal and confirmatory studies are needed to comprehensively delineate these associations. ConclusionOur study heralds dFC within the SN as a vital biomarker for cognitive impairment in pediatric epilepsy, advocating for targeted cognitive-specific interventions in managing BECTS-ESES. The preliminary nature of our findings invites further studies to substantiate these associations, offering profound implications for the prognosis and therapeutic strategies in BECTS-ESES, thereby underlining the importance of this research in the field of pediatric neurology and epilepsy management.

Characterization and classification of kinesthetic motor imagery levels

Objective. Kinesthetic Motor Imagery (KMI) represents a robust brain paradigm intended for electroencephalography (EEG)-based commands in brain-computer interfaces (BCIs). However, ensuring high accuracy in multi-command execution remains challenging, with data from C3 and C4 electrodes reaching up to 92% accuracy. This paper aims to characterize and classify EEG-based KMI of multilevel muscle contraction without relying on primary motor cortex signals. Approach. A new method based on Hurst exponents is introduced to characterize EEG signals of multilevel KMI of muscle contraction from electrodes placed on the premotor, dorsolateral prefrontal, and inferior parietal cortices. EEG signals were recorded during a hand-grip task at four levels of muscle contraction (0%, 10%, 40%, and 70% of the maximal isometric voluntary contraction). The task was executed under two conditions: first, physically, to train subjects in achieving muscle contraction at each level, followed by mental imagery under the KMI paradigm for each contraction level. EMG signals were recorded in both conditions to correlate muscle contraction execution, whether correct or null accurately. Independent component analysis (ICA) maps EEG signals from the sensor to the source space for preprocessing. For characterization, three algorithms based on Hurst exponents were used: the original (HO), using partitions (HRS), and applying semivariogram (HV). Finally, seven classifiers were used: Bayes network (BN), naive Bayes (NB), support vector machine (SVM), random forest (RF), random tree (RT), multilayer perceptron (MP), and k-nearest neighbors (kNN). Main results. A combination of the three Hurst characterization algorithms produced the highest average accuracy of 96.42% from kNN, followed by MP (92.85%), SVM (92.85%), NB (91.07%), RF (91.07%), BN (91.07%), and RT (80.35%). of 96.42% for kNN. Significance. Results show the feasibility of KMI multilevel muscle contraction detection and, thus, the viability of non-binary EEG-based BCI applications without using signals from the motor cortex.

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.

Flow augmentation therapies preserve brain network integrity and hemodynamics in a canine permanent occlusion model

The acute phase of ischemic stroke presents a critical window for therapeutic intervention, where novel approaches such as hyper-acute cerebral flow augmentation offer promising avenues for neuroprotection. In this study, we investigated the effects of two such therapies, NEH (a combination of norepinephrine and hydralazine) and Sanguinate (pegylated bovine carboxyhemoglobin), on resting-state functional connectivity, global mean signal (GMS), and blood oxygen level-dependent (BOLD) time lag in a pre-clinical canine model of stroke via permanent occlusion of the middle cerebral artery (total of n = 40 IACUC-approved mongrel canines randomly split into control/natural history and two treatment groups). Utilizing group independent component analysis (ICA), we identified and examined the integrity of sensorimotor and visual networks both pre- and post-occlusion, across treatment and control groups. Our results demonstrated that while the control group exhibited significant disruptions in these networks following stroke, the treatment groups showed remarkable preservation of network integrity. Voxel-wise functional connectivity analysis revealed less pronounced alterations in the treatment groups, suggesting maintained neural connections. Notably, the treatments stabilized GMS, with only minimal reductions observed post-occlusion compared to significant decreases in the control group. Furthermore, BOLD time-lag unity plots indicated that NEH and Sanguinate maintained consistent hemodynamic response timing, as evidenced by tighter clustering around the line of unity, suggesting a potential neuroprotective effect. These findings were underscored by robust statistical analyses, including paired T-tests and Mann–Whitney U tests, which confirmed the significance of the connectivity changes observed. The correlation of BOLD time-lag variations with neuroimaging functional biomarkers highlighted the impact of stroke and the efficacy of early therapeutic interventions. Our study supports the further study of flow augmentation therapies such as NEH and Sanguinate in stroke treatment protocols and suggests flow augmentation therapies should be further explored in an effort to improve patient outcomes.

Clinical utility of fMRI in evaluating of LSD effect on pain-related brain networks in healthy subjects

ObjectiveWe 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). MethodsTwenty 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). ResultsALFF 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. ConclusionThis 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.

Rorschach and resting state-fMRI: Personality Assessment and unique associations with resting-state brain networks activity

Background The Rorschach test is one of the most popular tests used in clinical settings for psychopathology and personality assessment; however, there is still little evidence regarding the functional correlates of test responses. Functional magnetic resonance imaging (fMRI) allows for the investigation of biological correlates associated with different psychological functions. Among other applications, fMRI has been used to identify different networks that reflect brain activity in the resting state (rs-fMRI), that is, when an individual is not engaged in any specific task. Among these resting-state networks (RSN), the best-known and most studied are the Default Mode Network (DMN), along with other networks such as salience, frontoparietal, sensorimotor, temporo-parietal, visual, and cerebellar networks. Methods We used this type of analysis to obtain new evidence regarding Rorschach. This study aimed to analyze the functional brain architecture underlying Rorschach’s personality variables, administered according to Exner’s Comprehensive System (CS). In particular, the aims are: 1) to ascertain the existence of correlations between CS variables and activity of the RSN, and 2) to use these profiles of activity to develop a new data-driven clustering of the CS variables. Archival data from twenty-four non-clinical subjects were analyzed. Independent Component Analysis (ICA) and partial least squares regression (PLS) were used to analyze the fMRI data. Results The results showed specific associations with the given Rorschach variables (several of which could be grouped into higher-order latent factors) and activity of the main RSN. Moreover, the cluster analysis outlined important groupings of Rorschach variables, particularly regarding their clinical implications. Conclusions Our study could be comprised in the existing literature providing strong evidence about the neurobiological validity of the Rorschach test.