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Applied Sciences
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Brain Functional Connectivity: Prediction, Dynamics, and Modeling—2nd Edition
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Brain Functional Connectivity: Prediction, Dynamics, and Modeling—2nd Edition

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: 30 October 2026 | Viewed by 6904

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,

This Special Issue is a continuation of our previous Special Issue "Brain Functional Connectivity: Prediction, Dynamics, and Modeling".

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. This Special Issue aims 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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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

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Related Special Issue

Published Papers (4 papers)

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Research

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11 pages, 1228 KB  
Article
Changes in Resting-State Connectivity After rTMS and Exercise in Persons with Post-Stroke Headache Pain
by Keith M. McGregor, Sarah K. Sweatt, Charity J. Morgan, Ayat Najmi, Marshall T. Holland, Joe R. Nocera and Chen Lin
Appl. Sci. 2026, 16(2), 985; https://doi.org/10.3390/app16020985 - 19 Jan 2026
Viewed by 582
Abstract
Chronic post-stroke headache is a common yet understudied complication of stroke, potentially driven by maladaptive connectivity between limbic and sensorimotor brain regions. This pilot study evaluated the effects of a combined intervention using repetitive transcranial magnetic stimulation (rTMS) and moderate-intensity exercise on resting-state [...] Read more.
Chronic post-stroke headache is a common yet understudied complication of stroke, potentially driven by maladaptive connectivity between limbic and sensorimotor brain regions. This pilot study evaluated the effects of a combined intervention using repetitive transcranial magnetic stimulation (rTMS) and moderate-intensity exercise on resting-state functional connectivity and self-reported pain outcomes in individuals with persistent post-stroke headache. Five participants completed ten sessions of rTMS targeted to the primary motor cortex followed by aerobic exercise within a 2 h window. Resting-state fMRI and behavioral data were collected at baseline and post-intervention. Seed-based analyses revealed reduced connectivity between the amygdala, insula, and thalamus and regions involved in salience, sensory, and cognitive control. Self-reported pain severity and interference (Brief Pain Inventory [BPI] and Visual Analogue Scale [VAS]) also showed mean reductions over the course of the study. These findings support the feasibility and potential neural and behavioral impact of combined neuromodulatory and behavioral interventions for managing chronic pain after stroke. Full article
(This article belongs to the Special Issue Brain Functional Connectivity: Prediction, Dynamics, and Modeling—2nd Edition)
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20 pages, 7063 KB  
Article
Effective Brain Connectivity Analysis During Endogenous Selective Attention Based on Granger Causality
by Walter Escalante Puente de la Vega and Alexander N. Pisarchik
Appl. Sci. 2026, 16(1), 101; https://doi.org/10.3390/app16010101 - 22 Dec 2025
Cited by 3 | Viewed by 1290
Abstract
Endogenous selective attention, the cognitive process of selectively attending to non-literal, ambiguous, or multistable interpretations of sensory input, remains poorly understood at the network level. To address this gap, we applied Granger causality (GC) analysis to electroencephalographic (EEG) recordings to characterize effective connectivity [...] Read more.
Endogenous selective attention, the cognitive process of selectively attending to non-literal, ambiguous, or multistable interpretations of sensory input, remains poorly understood at the network level. To address this gap, we applied Granger causality (GC) analysis to electroencephalographic (EEG) recordings to characterize effective connectivity during sustained attention to ambiguous visual stimuli. Participants viewed the Necker cube, whose left and right faces were modulated at 6.67 Hz and 8.57 Hz, respectively, enabling objective tracking of perceptual dominance via steady-state visually evoked potentials (SSVEPs). GC analysis revealed robust directed connectivity between frontal and occipito-parietal areas during sustained perception of a specific cube orientation. We found that the magnitude of the GC-derived F-statistics correlated positively with attention performance indices during the left-face orientation task and negatively during the right-face orientation task, indicating that interregional causal influence scales with cognitive engagement in ambiguous interpretation. These results establish GC as a sensitive and reliable approach for characterizing dynamic, directional neural interactions during perceptual ambiguity, and, most notably, reveal, for the first time, an occipito-frontal effective connectivity architecture specifically recruited in support of endogenous selective attention. The methodology and findings hold translational potential for applications in neuroadaptive interfaces, cognitive diagnostics, and the study of disorders involving impaired symbolic processing. Full article
(This article belongs to the Special Issue Brain Functional Connectivity: Prediction, Dynamics, and Modeling—2nd Edition)
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19 pages, 4944 KB  
Article
Spectrogram Contrast Enhancement Improves EEG Signal-Based Emotional Classification
by Fahad Layth Malallah and Kamran Iqbal
Appl. Sci. 2025, 15(23), 12634; https://doi.org/10.3390/app152312634 - 28 Nov 2025
Viewed by 1720
Abstract
Neuroscience adopts a multidimensional approach to decode thoughts and actions originating inside the brain, also called Brain Computer Interface (BCI). However, achieving high accuracy in the electroencephalography signal-based decoding remains a challenge and an open research topic in BCI research. This study aims [...] Read more.
Neuroscience adopts a multidimensional approach to decode thoughts and actions originating inside the brain, also called Brain Computer Interface (BCI). However, achieving high accuracy in the electroencephalography signal-based decoding remains a challenge and an open research topic in BCI research. This study aims to enhance the accuracy of signal classification for identifying human emotional states. We utilized the publicly available EEG–Audio–Video (EAV) dataset that comprises EEG recordings from 42 subjects across five emotional categories. Our key contribution is to exploit the two-dimensional contrast enhancement applied to the spectrogram for feature extraction, followed by classification using the EEGNet model. As a result, 12.5% improvement in classification accuracy over the baseline was achieved. This contribution demonstrates a potential advancement in BCI-based EEG signal processing in neuroscientific research. Full article
(This article belongs to the Special Issue Brain Functional Connectivity: Prediction, Dynamics, and Modeling—2nd Edition)
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Review

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18 pages, 882 KB  
Review
Synchronization, Information, and Brain Dynamics in Consciousness Research
by Francisco J. Esteban, Eva Vargas, José A. Langa and Fernando Soler-Toscano
Appl. Sci. 2026, 16(2), 1056; https://doi.org/10.3390/app16021056 - 20 Jan 2026
Viewed by 2243
Abstract
Understanding consciousness requires bridging theoretical models and clinically measurable brain dynamics. This review integrates three complementary frameworks that converge on a dynamical view of conscious processing: continuous formulations of Integrated Information Theory (IIT), attractor-landscape modeling of brain-state transitions, and perturbational complexity metrics from [...] Read more.
Understanding consciousness requires bridging theoretical models and clinically measurable brain dynamics. This review integrates three complementary frameworks that converge on a dynamical view of conscious processing: continuous formulations of Integrated Information Theory (IIT), attractor-landscape modeling of brain-state transitions, and perturbational complexity metrics from transcranial magnetic stimulation combined with electroencephalography (TMS-EEG). Continuous-time IIT formalizes how integrated information evolves across temporal hierarchies, while dynamical-systems approaches show that consciousness emerges near criticality, where metastable attractors enable flexible transitions between partially synchronized states. Perturbational-complexity indices capture these properties empirically, quantifying the brain’s capacity for integration and differentiation even without behavioral responsiveness. Across anesthesia, disorders of consciousness, epilepsy, and neurodegeneration, TMS-EEG biomarkers reveal reduced complexity and altered synchronization consistent with structural and functional disconnection. Integrating multimodal data—diffusion MRI, fMRI, EEG, and causal perturbations—is consistent with individualized modeling of consciousness-related dynamics. Standardized protocols, mechanistically interpretable machine learning, and longitudinal validation are essential for clinical translation. By uniting information-theoretic, dynamical, and empirical perspectives, this framework offers a reproducible foundation for consciousness biomarkers that mechanistically link brain dynamics to subjective experience, paving the way for precision applications in neurology, psychiatry, and anesthesia. Full article
(This article belongs to the Special Issue Brain Functional Connectivity: Prediction, Dynamics, and Modeling—2nd Edition)
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