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Applied Sciences
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Brain Functional Connectivity: Prediction, Dynamics, and Modeling
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Brain Functional Connectivity: Prediction, Dynamics, and Modeling

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Neuroscience and Neural Engineering".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 1681

Special Issue Editor

Prof. Dr. Alexander N. Pisarchik

E-Mail Website
Guest Editor
Center for Biomedical Technology, Technical University of Madrid, Campus Montegancedo, Pozuelo de Alarcón, 28223 Madrid, Spain
Interests: complex systems; bioinformatics; mathematical and computational biology; optics and photonics; biological physics; cognitive neuroscience
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The brain is one of the most complex and mysterious systems in the world. Functional connectivity can be studied in both the frequency and time domains using methods such as coherence, correlation, and artificial neural networks. Revealing the functional connectivity between different brain regions can help us understand the mechanisms underlying information processing and decision making during cognitive tasks. This knowledge can also address practical and challenging problems in various fields, including healthcare, medicine, biomedical engineering, brain–machine interfaces, and cognitive sciences. The aim of this Special Issue is to collect the best papers on recent advances and perspectives in brain connectivity research, encompassing theoretical modeling, experimental studies, and the analysis of neurophysiological data obtained using various brain imaging modalities.

Prof. Dr. Alexander N. Pisarchik
Guest Editor

Manuscript Submission Information

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Keywords

  • neuroimaging data analysis
  • neurophysiological signal processing
  • brain dynamics
  • brain networks
  • brain modeling
  • brain deseases
  • connectomics
  • neuronal synchronization
  • brain–machine interface
  • cognitive neuroscience
  • deep learning in neuroscience

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Published Papers (2 papers)

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Research

16 pages, 1390 KiB  
Article
Neural and Cardio-Respiratory Responses During Maximal Self-Paced and Controlled-Intensity Protocols at Similar Perceived Exertion Levels: A Pilot Study
by Luc Poinsard, Florent Palacin, Iraj Said Hashemi and Véronique Billat
Appl. Sci. 2024, 14(22), 10551; https://doi.org/10.3390/app142210551 - 15 Nov 2024
Viewed by 439
Abstract
Self-paced exercise protocols have gained attention for their potential to optimize performance and manage fatigue by allowing individuals to regulate their efforts based on perceived exertion. This pilot study aimed to investigate the neural and physiological responses during a self-paced V˙O [...] Read more.
Self-paced exercise protocols have gained attention for their potential to optimize performance and manage fatigue by allowing individuals to regulate their efforts based on perceived exertion. This pilot study aimed to investigate the neural and physiological responses during a self-paced V˙O2max (SPV) and incremental exercise tests (IET). Six trained male cyclists (mean age 39.2 ± 13.3 years; V˙O2max 54.3 ± 8.2 mL·kg−1·min−1) performed both tests while recording their brain activity using electroencephalography (EEG). The IET protocol involved increasing the power every 3 min relative to body weight, while the SPV allowed participants to self-regulate the intensity using ratings of perceived exertion (RPE). Gas exchange, EEG, heart rate (HR), stroke volume (SV), and power output were continuously monitored. Statistical analyses included a two-way repeated measures ANOVA and Wilcoxon signed-rank tests to assess differences in alpha and beta power spectral densities (PSDs) and the EEG/V˙O2 ratio. Our results showed that during the SPV test, the beta PSD initially increased but stabilized at around 80% of the test duration, suggesting effective management of effort without further neural strain. In contrast, the IET showed a continuous increase in beta activity, indicating greater neural demand and potentially leading to an earlier onset of fatigue. Additionally, participants maintained similar cardiorespiratory parameters (V˙O2, HR, SV, respiratory frequency, etc.) across both protocols, reinforcing the reliability of the RPE scale in guiding exercise intensity. These findings suggest that SPV better optimizes neural efficiency and delays fatigue compared to fixed protocols and that individuals can accurately control exercise intensity based on perceived exertion. Despite the small sample size, the results provide valuable insights into the potential benefits of self-paced exercise for improving adherence to exercise programs and optimizing performance across different populations. Full article
(This article belongs to the Special Issue Brain Functional Connectivity: Prediction, Dynamics, and Modeling)
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27 pages, 3762 KiB  
Article
Multi-Graph Assessment of Temporal and Extratemporal Lobe Epilepsy in Resting-State fMRI
by Dimitra Amoiridou, Kostakis Gkiatis, Ioannis Kakkos, Kyriakos Garganis and George K. Matsopoulos
Appl. Sci. 2024, 14(18), 8336; https://doi.org/10.3390/app14188336 - 16 Sep 2024
Viewed by 738
Abstract
Epilepsy is a common neurological disorder that affects millions of people worldwide, disrupting brain networks and causing recurrent seizures. In this regard, investigating the distinctive characteristics of brain connectivity is crucial to understanding the underlying neural processes of epilepsy. However, the various graph-theory [...] Read more.
Epilepsy is a common neurological disorder that affects millions of people worldwide, disrupting brain networks and causing recurrent seizures. In this regard, investigating the distinctive characteristics of brain connectivity is crucial to understanding the underlying neural processes of epilepsy. However, the various graph-theory frameworks and different estimation measures may yield significant variability among the results of different studies. On this premise, this study investigates the brain network topological variations between patients with temporal lobe epilepsy (TLE) and extratemporal lobe epilepsy (ETLE) using both directed and undirected network connectivity methods as well as different graph-theory metrics. Our results reveal distinct topological differences in connectivity graphs between the two epilepsy groups, with TLE patients displaying more disassortative graphs at lower density levels compared to ETLE patients. Moreover, we highlight the variations in the hub regions across different network metrics, underscoring the importance of considering various centrality measures for a comprehensive understanding of brain network dynamics in epilepsy. Our findings suggest that the differences in brain network organization between TLE and ETLE patients could be attributed to the unique characteristics of each epilepsy type, offering insights into potential biomarkers for type-specific epilepsy diagnosis and treatment. Full article
(This article belongs to the Special Issue Brain Functional Connectivity: Prediction, Dynamics, and Modeling)
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