Complexity Measures Of Psychotic Brain Activity In The fMRI Signal
Li, Q.; Seraji, M.; Calhoun, V.; Iraji, A.
Show abstract
When viewing the brain as a sophisticated, nonlinear dynamic system, employing complexity measures offers a valuable way to measure the intricate and dynamic aspects of spontaneous psychotic brain activity. These measures can help us identify irregularities and patterns in complex systems. In our study, we utilized fuzzy recurrence plots and sample entropy to evaluate the dynamic characteristics of psychiatric disorders. This assessment focused on understanding the temporal and spatial neural activity patterns, and more specifically, we applied complexity measures to investigate the functional connectivity within the psychotic brain. This involves understanding how different brain regions synchronize their activity, and complexity measures can reveal the patterns of these connections. It provides a means to understand how different brain regions interact and communicate under resting-state abnormal conditions. This study offers evidence demonstrating that fuzzy recurrence plots can serve as descriptors for functional connectivity and discusses their relevance to sample entropy in the context of the psychotic brain. In summary, complexity measures offer valuable insights that enrich our comprehension of atypical brain activity and the complexities present in the psychotic brain1.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A Transfer Entropy-based methodology to analyze information flow under eyes-open and eyes-closed conditions with a clinical perspective 96%
- Spectral Representation of EEG Data using Learned Graphs with Application to Motor Imagery Decoding 96%
- Local and Global Measures of Information Storage for the Assessment of Heartbeat-Evoked Cortical Responses 95%
Similar papers in this journal
- Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments 95%
- Automatic diagnostics of electroencephalography pathology based on multi-domain feature fusion 95%
- Eigenvector alignment: assessing functional network changes in amnestic mild cognitive impairment and Alzheimer's disease 94%
Similar papers in this journal
- Modeling and Analyzing Neural Signals with Phase Variability using the Fisher-Rao Registration 96%
- Holo-Hilbert Spectral-based Noise Removal Method for EEG High-Frequency Bands 95%
- A novel 5D brain parcellation approach based on spatio-temporal encoding of resting fMRI data from deep residual learning 95%
Similar papers in this journal
- Connectivity-based Meta-Bands: A new approach for automatic frequency band identification in connectivity analyses 95%
- Discovering hidden brain network responses to naturalistic stimuli via tensor component analysis of multi-subject fMRI data 95%
- Pumping Up your Predictive Power for Cognitive State Detection with the Proper GAINS 94%
Similar papers in this journal
- Unraveling Integration-Segregation Imbalances in Schizophrenia Through Topological High-Order Functional Connectivity 96%
- Complexity of brain dynamics as a correlate of consciousness in anaesthetized monkeys 95%
- Regularized Bagged Canonical Component Analysis for Multiclass Learning in Brain Imaging 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.