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Applied Sciences
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Computational and Mathematical Methods for Neuroscience
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Computational and Mathematical Methods for Neuroscience

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: 31 July 2024 | Viewed by 7411

Special Issue Editor

Prof. Dr. Alexander N. Pisarchik

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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 dedicated to exploring the profound impact of computational and mathematical methods in the realm of neuroscience. It showcases the integration of state-of-the-art techniques, including computational modeling, machine learning, network analysis, and brain–computer interfaces, which have significantly advanced our comprehension of brain dynamics, network interactions, cognitive functions, and behavior. The issue critically addresses the challenges related to data integration and model validation, underscoring the potential for groundbreaking discoveries that hold far-reaching implications for medicine, technology, and our overall understanding of the human mind. We invite you to contribute your original research or review papers that delve into innovative physical, mathematical, biological, and medical approaches. Submissions showcasing cutting-edge technology and important applications are warmly welcomed. Through our collective effort, we aspire to improve our understanding of brain functionality and propel neuroscience into new frontiers of knowledge.

Prof. Dr. Alexander N. Pisarchik
Guest Editor

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. 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

  • computational modeling
  • mathematical methods
  • machine learning
  • neural circuits
  • brain dynamics
  • network analysis
  • cognitive functions
  • behavioral modeling
  • brain–computer interfaces
  • brain–computer communication
  • brain imaging
  • neural data analysis
  • brain network organization
  • neural prosthetics
  • cognitive enhancement

Published Papers (10 papers)

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Research

Jump to: Review

17 pages, 5929 KiB  
Article
PadGAN: An End-to-End dMRI Data Augmentation Method for Macaque Brain
by Yifei Chen, Limei Zhang, Xiaohong Xue, Xia Lu, Haifang Li and Qianshan Wang
Appl. Sci. 2024, 14(8), 3229; https://0-doi-org.brum.beds.ac.uk/10.3390/app14083229 - 11 Apr 2024
Viewed by 307
Abstract
Currently, an increasing number of macaque brain MRI datasets are being made publicly accessible. Unlike human, publicly accessible macaque brain datasets suffer from data quality in diffusion magnetic resonance imaging (dMRI) data. Typically, dMRI data require a minimum ratio of 1:10 between low [...] Read more.
Currently, an increasing number of macaque brain MRI datasets are being made publicly accessible. Unlike human, publicly accessible macaque brain datasets suffer from data quality in diffusion magnetic resonance imaging (dMRI) data. Typically, dMRI data require a minimum ratio of 1:10 between low b-value (b < 10) volumes and high b-value (b > 300) volumes. However, the currently accessible macaque datasets do not meet this ratio. Due to site differences in macaque brain images, traditional human brain image-to-image translation models struggle to perform well on macaque brain images. Our work introduces a novel end-to-end primary-auxiliary dual generative adversarial network (PadGAN) for generating low b-value images. The auxiliary generator in the PadGAN is responsible for extracting the latent space features from peak information maps and transmitting them to the primary generator, enabling the primary generator to generate images with rich details. Experimental results demonstrate that PadGAN outperforms existing methods both qualitatively and quantitatively (mean SSIM increased by 0.1139). Diffusion probabilistic tractography using dMRI data augmented by our method yields superior results. Full article
(This article belongs to the Special Issue Computational and Mathematical Methods for Neuroscience)
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13 pages, 4148 KiB  
Article
Exploring EEG Emotion Recognition through Complex Networks: Insights from the Visibility Graph of Ordinal Patterns
by Longxin Yao, Yun Lu, Mingjiang Wang, Yukun Qian and Heng Li
Appl. Sci. 2024, 14(6), 2636; https://0-doi-org.brum.beds.ac.uk/10.3390/app14062636 - 21 Mar 2024
Viewed by 513
Abstract
The construction of complex networks from electroencephalography (EEG) proves to be an effective method for representing emotion patterns in affection computing as it offers rich spatiotemporal EEG features associated with brain emotions. In this paper, we propose a novel method for constructing complex [...] Read more.
The construction of complex networks from electroencephalography (EEG) proves to be an effective method for representing emotion patterns in affection computing as it offers rich spatiotemporal EEG features associated with brain emotions. In this paper, we propose a novel method for constructing complex networks from EEG signals for emotion recognition, which begins with phase space reconstruction to obtain ordinal patterns and subsequently forms a graph network representation from the sequence of ordinal patterns based on the visibility graph method, named ComNet-PSR-VG. For the proposed ComNet-PSR-VG, the initial step involves mapping EEG signals into a series of ordinal partitions using phase space reconstruction, generating a sequence of ordinal patterns. These ordinal patterns are then quantified to form a symbolized new sequence. Subsequently, the resulting symbolized sequence of ordinal patterns is transformed into a graph network using the visibility graph method. Two types of network node measures, average node degree (AND) and node degree entropy (NDE), are extracted from the graph networks as the inputs of machine learning for EEG emotion recognition. To evaluate the effectiveness of the proposed construction method of complex networks based on the visibility graph of ordinal patterns, comparative experiments are conducted using two types of simulated signals (random and Lorenz signals). Subsequently, EEG emotion recognition is performed on the SEED EEG emotion dataset. The experimental results show that, with AND as the feature, our proposed method is 4.88% higher than the existing visibility graph method and 12.23% higher than the phase space reconstruction method. These findings indicate that our proposed novel method for constructing complex networks from EEG signals not only achieves effective emotional EEG pattern recognition but also exhibits the potential for extension to other EEG pattern learning tasks, suggesting broad adaptability and application potential for our method. Full article
(This article belongs to the Special Issue Computational and Mathematical Methods for Neuroscience)
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19 pages, 7647 KiB  
Article
Hypergraph of Functional Connectivity Based on Event-Related Coherence: Magnetoencephalography Data Analysis
by Natalia Peña Serrano, Rider Jaimes-Reátegui and Alexander N. Pisarchik
Appl. Sci. 2024, 14(6), 2343; https://0-doi-org.brum.beds.ac.uk/10.3390/app14062343 - 11 Mar 2024
Viewed by 482
Abstract
We construct hypergraphs to analyze functional brain connectivity, leveraging event-related coherence in magnetoencephalography (MEG) data during the visual perception of a flickering image. Principal network characteristics are computed for the delta, theta, alpha, beta, and gamma frequency ranges. Employing a coherence measure, a [...] Read more.
We construct hypergraphs to analyze functional brain connectivity, leveraging event-related coherence in magnetoencephalography (MEG) data during the visual perception of a flickering image. Principal network characteristics are computed for the delta, theta, alpha, beta, and gamma frequency ranges. Employing a coherence measure, a statistical estimate of correlation between signal pairs across frequencies, we generate an edge time series, depicting how an edge evolves over time. This forms the basis for constructing an edge-to-edge functional connectivity network. We emphasize hyperedges as connected components in an absolute-valued functional connectivity network. Our coherence-based hypergraph construction specifically addresses functional connectivity among four brain lobes in both hemispheres: frontal, parietal, temporal, and occipital. This approach enables a nuanced exploration of individual differences within diverse frequency bands, providing insights into the dynamic nature of brain connectivity during visual perception tasks. The results furnish compelling evidence supporting the hypothesis of cortico–cortical interactions occurring across varying scales. The derived hypergraph illustrates robust activation patterns in specific brain regions, indicative of their engagement across diverse cognitive contexts and different frequency bands. Our findings suggest potential integration or multifunctionality within the examined lobes, contributing valuable perspectives to our understanding of brain dynamics during visual perception. Full article
(This article belongs to the Special Issue Computational and Mathematical Methods for Neuroscience)
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12 pages, 1516 KiB  
Article
Analysis of Head Micromovements and Body Posture for Vigilance Decrement Assessment
by Dario Rossi, Pietro Aricò, Gianluca Di Flumeri, Vincenzo Ronca, Andrea Giorgi, Alessia Vozzi, Rossella Capotorto, Bianca M. S. Inguscio, Giulia Cartocci, Fabio Babiloni and Gianluca Borghini
Appl. Sci. 2024, 14(5), 1810; https://0-doi-org.brum.beds.ac.uk/10.3390/app14051810 - 22 Feb 2024
Viewed by 638
Abstract
Vigilance refers to the capability of humans to respond accordingly to relevant and unpredictable tasks and surrounding environment changes over prolonged periods of time. Identifying vigilance decrements can, therefore, have huge and vital impacts on several operational environments in which a simple slip [...] Read more.
Vigilance refers to the capability of humans to respond accordingly to relevant and unpredictable tasks and surrounding environment changes over prolonged periods of time. Identifying vigilance decrements can, therefore, have huge and vital impacts on several operational environments in which a simple slip of mind or a deficit in attention can bear life-threatening and disastrous consequences. Several methodologies have been proposed to assess and characterize vigilance, and the results have indicated that the sole measure of performance and self-reports are not enough to obtain reliable and real-time vigilance measure. Nowadays, monitoring head and body movements to obtain information about performance in daily activities, health conditions, and mental states has become very simple and cheap due to the miniaturization of inertial measurement units and their widespread integration into common electronic devices (e.g., smart glasses, smartwatches). The present study aimed to understand the relationship between head micromovements and body posture changes to vigilance decrease while performing the psychomotor vigilance task. The results highlighted that head micromovements can be employed to track vigilance decrement during prolonged periods of time and discriminate between conditions of high or low vigilance. Full article
(This article belongs to the Special Issue Computational and Mathematical Methods for Neuroscience)
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11 pages, 1608 KiB  
Article
Somatosensory Mismatch Response in Patients with Cerebral Palsy
by Susmita Roy, Stefan K. Ehrlich and Renée Lampe
Appl. Sci. 2024, 14(3), 1030; https://0-doi-org.brum.beds.ac.uk/10.3390/app14031030 - 25 Jan 2024
Viewed by 613
Abstract
Background: Mismatch negativity (MMN), an event-related potential (ERP) component occurring at specific recording sites and latency, is associated with an automatic change detection response, generally elicited using oddball paradigms wherein infrequent stimuli are embedded in repeated, frequent stimuli. To verify the presence of [...] Read more.
Background: Mismatch negativity (MMN), an event-related potential (ERP) component occurring at specific recording sites and latency, is associated with an automatic change detection response, generally elicited using oddball paradigms wherein infrequent stimuli are embedded in repeated, frequent stimuli. To verify the presence of mismatch-related ERP responses to somatosensory stimulation in individuals with cerebral palsy (CP), we conducted a preliminary study involving healthy participants and patients with CP. Methods: Both groups underwent ‘frequent’ and ’infrequent’ stimulation applied to the ring finger and thumb of their left hand, respectively. ERPs were recorded at frontal, central, and parietal scalp locations using electroencephalography. A healthy cohort tested the experimental protocol and showed evidence that mismatch-related ERP responses were observable. Subsequent analysis focused on the patient group. Results: Statistically significant differences between the two types of stimuli were observed on the frontocentral and parietal channels between 150 and 250 ms after the stimulus onset in the patient group. Furthermore, a late discriminative response was observed in the frontal and parietal channels. Conclusion: The results demonstrate the presence of mismatch-related ERP responses in individuals with CP. Full article
(This article belongs to the Special Issue Computational and Mathematical Methods for Neuroscience)
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19 pages, 1775 KiB  
Article
Partial Parallelism Plots
by Axel Petzold
Appl. Sci. 2024, 14(2), 602; https://0-doi-org.brum.beds.ac.uk/10.3390/app14020602 - 10 Jan 2024
Viewed by 540
Abstract
Demonstrating parallelism in quantitative laboratory tests is crucial to ensure accurate reporting of data and minimise risks to patients. Regulatory authorities make the demonstration of parallelism before clinical use approval mandate. However, achieving statistical parallelism can be arduous, especially when parallelism is limited [...] Read more.
Demonstrating parallelism in quantitative laboratory tests is crucial to ensure accurate reporting of data and minimise risks to patients. Regulatory authorities make the demonstration of parallelism before clinical use approval mandate. However, achieving statistical parallelism can be arduous, especially when parallelism is limited to a subrange of the data. To address potential biases and confounds, I propose a simple graphical method, the Partial Parallelism Plot, to demonstrate partial parallelism. The proposed method offers ease of understanding, intuitiveness, and graphical simplicity. It enables the graphical assessment of quantitative data risk when parallelism is lacking within a defined range. As parallelism may not be consistent across the entire analytical range, the plots focus on partial parallelism. The method can readily be programmed into graphical applications for enhanced interactivity. By providing a clear graphical representation, the method allows researchers to ascertain the presence of parallelism in laboratory tests, thus aiding in the validation process for trials and clinical applications. Full article
(This article belongs to the Special Issue Computational and Mathematical Methods for Neuroscience)
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19 pages, 1949 KiB  
Article
Convolutional Neural Network-Based Classification of Steady-State Visually Evoked Potentials with Limited Training Data
by Marcin Kołodziej, Andrzej Majkowski, Remigiusz J. Rak and Przemysław Wiszniewski
Appl. Sci. 2023, 13(24), 13350; https://0-doi-org.brum.beds.ac.uk/10.3390/app132413350 - 18 Dec 2023
Viewed by 1034
Abstract
One approach employed in brain–computer interfaces (BCIs) involves the use of steady-state visual evoked potentials (SSVEPs). This article examines the capability of artificial intelligence, specifically convolutional neural networks (CNNs), to improve SSVEP detection in BCIs. Implementing CNNs for this task does not require [...] Read more.
One approach employed in brain–computer interfaces (BCIs) involves the use of steady-state visual evoked potentials (SSVEPs). This article examines the capability of artificial intelligence, specifically convolutional neural networks (CNNs), to improve SSVEP detection in BCIs. Implementing CNNs for this task does not require specialized knowledge. The subsequent layers of the CNN extract valuable features and perform classification. Nevertheless, a significant number of training examples are typically required, which can pose challenges in the practical application of BCI. This article examines the possibility of using a CNN in combination with data augmentation to address the issue of a limited training dataset. The data augmentation method that we applied is based on the spectral analysis of the electroencephalographic signals (EEG). Initially, we constructed the spectral representation of the EEG signals. Subsequently, we generated new signals by applying random amplitude and phase variations, along with the addition of noise characterized by specific parameters. The method was tested on a set of real EEG signals containing SSVEPs, which were recorded during stimulation by light-emitting diodes (LEDs) at frequencies of 5, 6, 7, and 8 Hz. We compared the classification accuracy and information transfer rate (ITR) across various machine learning approaches using both real training data and data generated with our augmentation method. Our proposed augmentation method combined with a convolutional neural network achieved a high classification accuracy of 0.72. In contrast, the linear discriminant analysis (LDA) method resulted in an accuracy of 0.59, while the canonical correlation analysis (CCA) method yielded 0.57. Additionally, the proposed approach facilitates the training of CNNs to perform more effectively in the presence of various EEG artifacts. Full article
(This article belongs to the Special Issue Computational and Mathematical Methods for Neuroscience)
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17 pages, 3061 KiB  
Article
Novel Deep-Learning Approach for Automatic Diagnosis of Alzheimer’s Disease from MRI
by Omar Altwijri, Reem Alanazi, Adham Aleid, Khalid Alhussaini, Ziyad Aloqalaa, Mohammed Almijalli and Ali Saad
Appl. Sci. 2023, 13(24), 13051; https://0-doi-org.brum.beds.ac.uk/10.3390/app132413051 - 07 Dec 2023
Cited by 1 | Viewed by 1133
Abstract
This study introduces a novel deep-learning methodology that is customized to automatically diagnose Alzheimer’s disease (AD) through the analysis of MRI datasets. The process of diagnosing AD via the visual examination of magnetic resonance imaging (MRI) presents considerable challenges. The visual diagnosis of [...] Read more.
This study introduces a novel deep-learning methodology that is customized to automatically diagnose Alzheimer’s disease (AD) through the analysis of MRI datasets. The process of diagnosing AD via the visual examination of magnetic resonance imaging (MRI) presents considerable challenges. The visual diagnosis of mild to very mild stages of AD is challenging due to the MRI similarities observed between a brain that is aging normally and one that has AD. The detection of AD with extreme precision is critical during its early stages. Deep-learning techniques have recently been shown to be significantly more effective than human detection in identifying various stages of AD, enabling early-stage diagnosis. The aim of this research is to develop a deep-learning approach that utilizes pre-trained convolutional neural networks (CNNs) to accurately detect the severity levels of AD, particularly in situations where the quantity and quality of available datasets are limited. In this approach, the AD dataset is preprocessed via a refined image processing module prior to the training phase. The proposed method was compared to two well-known deep-learning algorithms (VGG16 and ResNet50) using four Kaggle AD datasets: one for the normal stage of the disease and three for the mild, very mild, and moderate stages, respectively. This allowed us to evaluate the effectiveness of the classification results. The three models were compared using six performance metrics. The results achieved with our approach indicate an overall detection accuracy of 99.3%, which is superior to the other existing models. Full article
(This article belongs to the Special Issue Computational and Mathematical Methods for Neuroscience)
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Review

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18 pages, 2611 KiB  
Review
Design, Synthesis and Molecular Modeling Study of Radiotracers Based on Tacrine and Its Derivatives for Study on Alzheimer’s Disease and Its Early Diagnosis
by Przemysław Koźmiński and Ewa Gniazdowska
Appl. Sci. 2024, 14(7), 2827; https://0-doi-org.brum.beds.ac.uk/10.3390/app14072827 - 27 Mar 2024
Viewed by 357
Abstract
From 1993 to 2013, tacrine was an approved drug for Alzheimer’s disease. Due to its strong inhibitory properties towards cholinesterase, tacrine causes an increase in the level of the neurotransmitter acetylcholine in the cholinergic system of the central nervous system. This work presents [...] Read more.
From 1993 to 2013, tacrine was an approved drug for Alzheimer’s disease. Due to its strong inhibitory properties towards cholinesterase, tacrine causes an increase in the level of the neurotransmitter acetylcholine in the cholinergic system of the central nervous system. This work presents a review of articles in which tacrine or its derivatives labeled with the radionuclides 3H, 11C, 14C, 123I, 99mTc and 68Ga were used as vectors in radiotracers dedicated to the diagnosis of Alzheimer’s disease. The possibility of clinical applications of the obtained radiopreparations was assessed by analyzing their physicochemical properties, ability to cross the blood–brain barrier and the level of uptake in the brain. Based on these data, it was shown that radiopreparations based on the tacrine molecule or its very close analogues retain the ability to cross the blood–brain barrier, while radiopreparations containing a more modified tacrine molecule (connected via a linker to a radionuclide chelator) lose this ability. This is probably the result of the addition of a chelator, which significantly increases the size of the radiopreparation and reduces its lipophilicity. Computer docking studies of tacrine derivatives and/or radiopreparations showed how these compounds bind to the active sites of acetyl- and butyrylcholinesterase. Full article
(This article belongs to the Special Issue Computational and Mathematical Methods for Neuroscience)
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24 pages, 1250 KiB  
Review
Sensorial Feedback Contribution to the Sense of Embodiment in Brain–Machine Interfaces: A Systematic Review
by Diogo João Tomás, Miguel Pais-Vieira and Carla Pais-Vieira
Appl. Sci. 2023, 13(24), 13011; https://0-doi-org.brum.beds.ac.uk/10.3390/app132413011 - 06 Dec 2023
Cited by 1 | Viewed by 696
Abstract
The sense of embodiment (SoE) is an essential element of human perception that allows individuals to control and perceive the movements of their body parts. Brain–machine interface (BMI) technology can induce SoE in real time, and adding sensory feedback through various modalities has [...] Read more.
The sense of embodiment (SoE) is an essential element of human perception that allows individuals to control and perceive the movements of their body parts. Brain–machine interface (BMI) technology can induce SoE in real time, and adding sensory feedback through various modalities has been shown to improve BMI control and elicit SoEe. In this study, we conducted a systematic review to study BMI performance in studies that integrated SoE variables and analyzed the contribution of single or multimodal sensory stimulation. Out of 493 results, only 20 studies analyzed the SoE of humans using BMIs. Analysis of these articles revealed that 40% of the studies relating BMIs with sensory stimulation and SoE primarily focused on manipulating visual stimuli, particularly in terms of coherence (i.e., synchronous vs. asynchronous stimuli) and realism (i.e., humanoid or robotic appearance). However, no study has analyzed the independent contributions of different sensory modalities to SoE and BMI performance. These results suggest that providing a detailed description of the outcomes resulting from independent and combined effects of different sensory modalities on the experience of SoE during BMI control may be relevant for the design of neurorehabilitation programs. Full article
(This article belongs to the Special Issue Computational and Mathematical Methods for Neuroscience)
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