Altered brain entropy and functional connectivity patterns in generalized anxiety disorder patients

Abnormal brain entropy dynamics in ADHD

BACKGROUND

Brain entropy (BEN) is a novel measure for irregularity and complexity of brain activities, which has been used to characterize abnormal brain activities in many brain disorders including attention-deficit/hyperactivity disorder (ADHD). While most research assumes BEN is stationary during scan sessions, the brain in resting state is also a highly dynamic system. The BEN dynamics in ADHD has not been explored.

METHODS

We used a sliding window approach to derive the dynamical brain entropy (dBEN) from resting-state functional magnetic resonance imaging (rfMRI) dataset that includes 98 ADHD patients and 111 healthy controls (HCs). We identified 3 reoccurring BEN states. We tested whether the BEN dynamics differ between ADHD and HC, and whether they are associated with ADHD symptom severity.

RESULTS

One BEN states, characterized by low overall BEN and low within-state BEN located in SMN (sensorimotor network) and VN (visual network), its FW (fractional window) and MDT (mean dwell time) were increased in ADHD and positively correlated with ADHD severity; another state characterized by high overall BEN and low within-state BEN located in DMN (default mode network) and ECN (executive control network), its FW and MDT were decreased in ADHD and negatively correlated with ADHD severity.

LIMITATIONS

The window length of dBEN analysis can be further optimized to suit more datasets. The co-variation between dBEN and other dynamical brain metrics was not explored.

CONCLUSION

Our findings revealed abnormal BEN dynamics in ADHD, providing new insights into clinical diagnosis and neuropathology of ADHD.

Altered Resting-State Brain Entropy in Cerebral Small Vessel Disease Patients with Cognitive Impairment

Objective: Cerebral small vessel disease (CSVD) is a primary vascular disease of cognitive impairment. Previous studies have predominantly focused on brain linear features. However, the nonlinear measure, brain entropy (BEN), has not been elaborated. Thus, this study aims to investigate if BEN abnormalities could manifest in CSVD patients with cognitive impairment. Methods: Thirty-four CSVD patients with cognitive impairment and 37 healthy controls (HCs) were recruited. Analysis of gray matter approximate entropy (ApEn) and sample entropy (SampEn) which are two indices of BEN was calculated. To explore whether BEN can provide unique information, we further performed brain linear methods, namely, amplitude of low frequency fluctuation (ALFF) and regional homogeneity (ReHo), to observe their differences. The ratios of BEN/ALFF and BEN/ReHo which represent the coupling of nonlinear and linear features were introduced. Correlation analysis was conducted between imaging indices and cognition. Subsequently, the linear support vector machine (SVM) was used to assess their discriminative ability. Results: CSVD patients exhibited lower ApEn and SamEn values in sensorimotor areas, which were correlated with worse memory and executive function. In addition, the results of BEN showed little overlap with ALFF and ReHo in brain regions. Correlation analysis also revealed a relationship between the two ratios and cognition. SVM analysis using BEN and its ratios as features achieved an accuracy of 74.64% (sensitivity: 86.49%, specificity: 61.76%, and AUC: 0.82439). Conclusion: Our study reveals that the reduction of sensorimotor system complexity is correlated with cognition. BEN exhibits distinctive characteristics in brain activity. Combining BEN and the ratios can be new biomarkers to diagnose CSVD with cognitive impairment. Impact Statement Cerebral small vessel disease (CSVD) is regarded as the most important vascular disease of cognitive impairment. However, conventional brain imaging fails to adequately elucidate the pathogenesis of cognitive disorder related to CSVD. In this regard, exploring brain entropy (BEN) based on resting-state functional magnetic resonance imaging (rs-fMRI) represents a relatively novel and unexplored approach in the context of CSVD. This approach provides novel insights into the pathogenesis, diagnosis, and rehabilitation of cognitive disorder associated with CSVD.

Review of the Brain's Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM)

The brain is the most complex organ in the human body and, as such, its study entails great challenges (methodological, theoretical, etc.). Nonetheless, there is a remarkable amount of studies about the consequences of pathological conditions on its development and functioning. This bibliographic review aims to cover mostly findings related to changes in the physical distribution of neurons and their connections-the connectome-both structural and functional, as well as their modelling approaches. It does not intend to offer an extensive description of all conditions affecting the brain; rather, it presents the most common ones. Thus, here, we highlight the need for accurate brain modelling that can subsequently be used to understand brain function and be applied to diagnose, track, and simulate treatments for the most prevalent pathologies affecting the brain.

The Efficacy of Machine Learning Models for Predicting the Prognosis of Heart Failure: A Systematic Review and Meta-Analysis

INTRODUCTION

Heart failure (HF) is a major global public health concern. The application of machine learning (ML) to identify individuals at high risk and enable early intervention is a promising approach for improving HF prognosis. We aim to systematically evaluate the performance and value of ML models for predicting HF prognosis.

METHODS

PubMed, Web of Science, Scopus, and Embase online databases were searched up to April 30, 2023, to identify studies on the use of ML models to predict HF prognosis. HF prognosis primarily encompasses readmission and mortality. The meta-analysis was conducted by MedCalc software. Subgroup analyses include grouping based on types of ML models, time intervals, sample sizes, the number of predictive variables, validation methods, whether to conduct hyperparameter optimization and calibration, data set partitioning methods.

RESULTS

A total of 31 studies were included. The most common ML models were random forest, boosting, support vector machine, neural network. The area under the receiver operating characteristic curve (AUC) for predicting HF readmission was 0.675 (95% CI: 0.651-0.699, p < 0.001), and the AUC for predicting HF mortality was 0.790 (95% CI: 0.765-0.816, p < 0.001). Subgroup analyses revealed that models with the prediction time interval of 1 year, sample sizes ≥10,000, the number of predictive variables ≥100, external validation, hyperparameter tuning, calibration adjustment, and data set partitioning using 10-fold cross-validation exhibited favorable performance within their respective subgroups.

CONCLUSION

The performance of ML models in predicting HF readmission is relatively poor, while its performance in predicting HF mortality is moderate. The quality of the relevant studies is generally low, it is essential to enhance the predictive capabilities of ML models through targeted improvements in practical applications.

Changed brain entropy and functional connectivity patterns induced by electroconvulsive therapy in majoy depression disorder

OBJECTIVE

Our objective is to innovatively integrate both linear and nonlinear characteristics of brain signals in Electroconvulsive Therapy (ECT) research, with the goal of uncovering deeper insights into the pathogenesis of Major Depressive Disorder (MDD) and identifying novel targets for other physical intervention therapies.

METHODS

We measured brain entropy (BEN) in 42 MDD patients and 42 matched healthy controls (HC) using rs-fMRI data. Brain regions that differed significantly in patients with MDD before and after ECT were extracted. Then, we use these brain regions as seed points to investigate the differences in whole-brain resting-state functional connectivity (RSFC) patterns before and after ECT.

RESULTS

Compared to HCs, patients had higher BEN levels in the right precuneus (PCUN.R) and right angular gyrus (ANG.R). After ECT, patients had lower BEN levels in the PCUN.R and ANG.R. Compared with before ECT, patients showed significantly increased RSFC after ECT between the PCUN.R and right middle temporal gyrus and ANG.R. Significantly increased RSFC was observed between the ANG.R and right middle frontal gyrus and right supramarginal gyrus after ECT.

CONCLUSION

Combining the linear and nonlinear characteristics of brain signals can effectively explore the pathogenesis of depression and provide new targets for ECT.

Deficits in brain default mode network connectivity mediate the relationship between poor sleep quality and anxiety severity

STUDY OBJECTIVES

Chronic insomnia disorder (CID) is a prevalent sleep disorder that frequently cooccurs with anxiety. The association between insomnia and anxiety has been established; however, the neurobiological basis of this relationship remains unclear. This study aimed to investigate the neural markers of CID patients with and without anxiety and to determine whether specific neural connectivity mediates the relationship between insomnia and anxiety.

METHODS

This study included 180 participants, comprising CID patients with anxiety (CID-A), CID patients without anxiety (CID-NA), and good sleep controls. All participants completed self-reported measures of sleep quality and anxiety severity and underwent functional magnetic resonance imaging. Brain functional integration was measured using functional connectivity density (FCD) and resting-state functional connectivity (rsFC). Correlation and mediation analyses were used to examine the relationships among brain connectivity, sleep quality, and anxiety severity.

RESULTS

The CID-NA and CID-A groups showed decreased local FCD in the medial prefrontal cortex (mPFC) and disrupted rsFC between the precuneus and other brain regions. Only the CID-A group exhibited altered long-range FCD in the precuneus and the rsFC between the anterior default mode network (DMN, e.g. mPFC) and posterior DMN (e.g. precuneus). Mediation analysis revealed DMN dysconnectivity underlying the association between poor sleep quality and anxiety symptoms.

CONCLUSIONS

This study identified shared and distinct brain circuit disruptions in the CID-NA and CID-A groups, with deficits in DMN connectivity as a potential neural mechanism through which disrupted sleep augments anxiety. These findings may facilitate the development of personalized therapies for insomnia and associated anxiety problems.