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Brain Sciences
Special Issues
Computational Intelligence and Brain Plasticity
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Computational Intelligence and Brain Plasticity

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Computational Neuroscience and Neuroinformatics".

Deadline for manuscript submissions: 24 March 2025 | Viewed by 7548

Special Issue Editors

Dr. Gianluca Borghini

E-Mail Website
Guest Editor
Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
Interests: cognitive neuroscience; machine learning; neuroscience; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Riccardo Barbieri

E-Mail Website
Guest Editor
Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, 20133 Milan, Italy
Interests: signal processing algorithms for the analysis of biological systems; computational modeling of neural information encoding, and on application of nonlinear and multivariate statistical models to characterize heart rate variability and cardiovascular control dynamics
Dr. Viviana Mucci

E-Mail Website
Guest Editor
School of Science, Western Sydney University, Sydney, NSW 2751, Australia
Interests: pathophysiology of major central vestibular disorders; the investigation of dysautonomia and hormonal dysfunctions in vestibular patients; novel therapeutic approaches based on new pathophysiological considerations
Special Issues, Collections and Topics in MDPI journals
Dr. Hong Zeng

E-Mail Website
Guest Editor
College of Computer Science, Hangzhou Dianzi University, Second Street, Xiasha Higher Education Zone, Hangzhou 310018, China
Interests: brain
Dr. Alessandra Anzolin

E-Mail Website
Guest Editor
Spaulding Rehabilitation Hospital, Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA 02115, USA
Interests: brain networks; hyperscanning; signal-processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ability of the nervous system to change its activity in response to intrinsic or extrinsic stimuli and to reorganize its structure, functions, or connections is known as brain plasticity. Plasticity is crucial for recovering cognitive and motor functions after severe injuries, as well as for navigating unfamiliar situations while learning new tasks or skills. Recently, artificial intelligence (AI) and innovative computational methods have provided important tools for a quantitative assessment of brain plasticity. These measures can be used in clinical applications, for example, to assess patient progress during rehabilitation treatments and to develop patient-specific training sessions based on trainees’ performance and brain responses. Important examples of industrial applications include neuroimaging technology, wearable sensors, and advanced data processing. AI-based methodologies are some of the most appropriate tools for discovering the wide range of mechanisms underlying brain plasticity.

This Special Issue includes both methodological and experimental studies aimed at identifying changes in subjects’ brain activation patterns or mental states (e.g., learning, mental workload, stress, attention, mental fatigue, post-stroke, etc.) due to plasticity aspects. Studies on wearable sensors and associated technologies are also encouraged. All types of manuscripts will be taken into consideration, including original basic science reports, translational research, clinical studies, review articles, and methodology papers.

We welcome innovative research in the following fields: neuroscience, neurosurgery, neurorehabilitation, neural networks, and brain neuroplasticity with human–machine interactions at work.

Dr. Gianluca Borghini
Dr. Riccardo Barbieri
Dr. Viviana Mucci
Dr. Hong Zeng
Dr. Alessandra Anzolin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Brain Sciences is an international peer-reviewed open access monthly 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 2200 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

  • brain plasticity
  • learning process
  • cognitive training
  • mental state assessment
  • cognitive rehabilitation
  • high-resolution eeg
  • brain connectivity
  • cognitive control behaviour
  • artificial intelligence
  • hypers-canning
  • multivariate autoregressive models

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Published Papers (4 papers)

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Research

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18 pages, 2001 KiB  
Article
Multimodal Fusion of EEG and Audio Spectrogram for Major Depressive Disorder Recognition Using Modified DenseNet121
by Musyyab Yousufi, Robertas Damaševičius and Rytis Maskeliūnas
Brain Sci. 2024, 14(10), 1018; https://doi.org/10.3390/brainsci14101018 - 15 Oct 2024
Viewed by 2393
Abstract
Background/Objectives: This study investigates the classification of Major Depressive Disorder (MDD) using electroencephalography (EEG) Short-Time Fourier-Transform (STFT) spectrograms and audio Mel-spectrogram data of 52 subjects. The objective is to develop a multimodal classification model that integrates audio and EEG data to accurately identify [...] Read more.
Background/Objectives: This study investigates the classification of Major Depressive Disorder (MDD) using electroencephalography (EEG) Short-Time Fourier-Transform (STFT) spectrograms and audio Mel-spectrogram data of 52 subjects. The objective is to develop a multimodal classification model that integrates audio and EEG data to accurately identify depressive tendencies. Methods: We utilized the Multimodal open dataset for Mental Disorder Analysis (MODMA) and trained a pre-trained Densenet121 model using transfer learning. Features from both the EEG and audio modalities were extracted and concatenated before being passed through the final classification layer. Additionally, an ablation study was conducted on both datasets separately. Results: The proposed multimodal classification model demonstrated superior performance compared to existing methods, achieving an Accuracy of 97.53%, Precision of 98.20%, F1 Score of 97.76%, and Recall of 97.32%. A confusion matrix was also used to evaluate the model’s effectiveness. Conclusions: The paper presents a robust multimodal classification approach that outperforms state-of-the-art methods with potential application in clinical diagnostics for depression assessment. Full article
(This article belongs to the Special Issue Computational Intelligence and Brain Plasticity)
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27 pages, 3881 KiB  
Article
Neuroergonomic Attention Assessment in Safety-Critical Tasks: EEG Indices and Subjective Metrics Validation in a Novel Task-Embedded Reaction Time Paradigm
by Bojana Bjegojević, Miloš Pušica, Gabriele Gianini, Ivan Gligorijević, Sam Cromie and Maria Chiara Leva
Brain Sci. 2024, 14(10), 1009; https://doi.org/10.3390/brainsci14101009 - 7 Oct 2024
Viewed by 1710
Abstract
Background/Objectives: This study addresses the gap in methodological guidelines for neuroergonomic attention assessment in safety-critical tasks, focusing on validating EEG indices, including the engagement index (EI) and beta/alpha ratio, alongside subjective ratings. Methods: A novel task-embedded reaction time paradigm was developed to evaluate [...] Read more.
Background/Objectives: This study addresses the gap in methodological guidelines for neuroergonomic attention assessment in safety-critical tasks, focusing on validating EEG indices, including the engagement index (EI) and beta/alpha ratio, alongside subjective ratings. Methods: A novel task-embedded reaction time paradigm was developed to evaluate the sensitivity of these metrics to dynamic attentional demands in a more naturalistic multitasking context. By manipulating attention levels through varying secondary tasks in the NASA MATB-II task while maintaining a consistent primary reaction-time task, this study successfully demonstrated the effectiveness of the paradigm. Results: Results indicate that both the beta/alpha ratio and EI are sensitive to changes in attentional demands, with beta/alpha being more responsive to dynamic variations in attention, and EI reflecting more the overall effort required to sustain performance, especially in conditions where maintaining attention is challenging. Conclusions: The potential for predicting the attention lapses through integration of performance metrics, EEG measures, and subjective assessments was demonstrated, providing a more nuanced understanding of dynamic fluctuations of attention in multitasking scenarios, mimicking those in real-world safety-critical tasks. These findings provide a foundation for advancing methods to monitor attention fluctuations accurately and mitigate risks in critical scenarios, such as train-driving or automated vehicle operation, where maintaining a high attention level is crucial. Full article
(This article belongs to the Special Issue Computational Intelligence and Brain Plasticity)
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25 pages, 9258 KiB  
Article
A Learning Dendritic Neuron-Based Motion Direction Detective System and Its Application to Grayscale Images
by Tianqi Chen, Yuki Todo, Ryusei Takano, Zhiyu Qiu, Yuxiao Hua and Zheng Tang
Brain Sci. 2024, 14(9), 864; https://doi.org/10.3390/brainsci14090864 - 27 Aug 2024
Cited by 1 | Viewed by 808
Abstract
In recent research, dendritic neuron-based models have shown promise in effectively learning and recognizing object motion direction within binary images. Leveraging the dendritic neuron structure and On–Off Response mechanism within the primary cortex, this approach has notably reduced learning time and costs compared [...] Read more.
In recent research, dendritic neuron-based models have shown promise in effectively learning and recognizing object motion direction within binary images. Leveraging the dendritic neuron structure and On–Off Response mechanism within the primary cortex, this approach has notably reduced learning time and costs compared to traditional neural networks. This paper advances the existing model by integrating bio-inspired components into a learnable dendritic neuron-based artificial visual system (AVS), specifically incorporating mechanisms from horizontal and bipolar cells. This enhancement enables the model to proficiently identify object motion directions in grayscale images, aligning its threshold with human-like perception. The enhanced model demonstrates superior efficiency in motion direction recognition, requiring less data (90% less than other deep models) and less time for training. Experimental findings highlight the model’s remarkable robustness, indicating significant potential for real-world applications. The integration of bio-inspired features not only enhances performance but also opens avenues for further exploration in neural network research. Notably, the application of this model to realistic object recognition yields convincing accuracy at nearly 100%, underscoring its practical utility. Full article
(This article belongs to the Special Issue Computational Intelligence and Brain Plasticity)
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Review

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21 pages, 296 KiB  
Review
The Role of Brain Plasticity in Neuromuscular Disorders: Current Knowledge and Future Prospects
by Paolo Alonge, Giulio Gadaleta, Guido Urbano, Antonino Lupica, Vincenzo Di Stefano, Filippo Brighina and Angelo Torrente
Brain Sci. 2024, 14(10), 971; https://doi.org/10.3390/brainsci14100971 - 26 Sep 2024
Viewed by 1138
Abstract
Background/Objectives: Increasing evidence shows an involvement of brain plasticity mechanisms in both motor and central manifestations of neuromuscular disorders (NMDs). These mechanisms could be specifically addressed with neuromodulation or rehabilitation protocols. The aim of this scoping review is to summarise the evidence [...] Read more.
Background/Objectives: Increasing evidence shows an involvement of brain plasticity mechanisms in both motor and central manifestations of neuromuscular disorders (NMDs). These mechanisms could be specifically addressed with neuromodulation or rehabilitation protocols. The aim of this scoping review is to summarise the evidence on plasticity mechanisms’ involvement in NMDs to encourage future research. Methods: A scoping review was conducted searching the PubMed and Scopus electronic databases. We selected papers addressing brain plasticity and central nervous system (CNS) studies through non-invasive brain stimulation techniques in myopathies, muscular dystrophies, myositis and spinal muscular atrophy. Results: A total of 49 papers were selected for full-text examination. Regardless of the variety of pathogenetic and clinical characteristics of NMDs, studies show widespread changes in intracortical inhibition mechanisms, as well as disruptions in glutamatergic and GABAergic transmission, resulting in altered brain plasticity. Therapeutic interventions with neurostimulation techniques, despite being conducted only anecdotally or on small samples, show promising results; Conclusions: despite challenges posed by the rarity and heterogeneity of NMDs, recent evidence suggests that synaptic plasticity may play a role in the pathogenesis of various muscular diseases, affecting not only central symptoms but also strength and fatigue. Key questions remain unanswered about the role of plasticity and its potential as a therapeutic target. As disease-modifying therapies advance, understanding CNS involvement in NMDs could lead to more tailored treatments. Full article
(This article belongs to the Special Issue Computational Intelligence and Brain Plasticity)
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