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Brain Sciences
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Neuroscience Meets Artificial Intelligence
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Neuroscience Meets Artificial Intelligence

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neural Engineering, Neuroergonomics and Neurorobotics".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 3424

Special Issue Editors

Dr. Christian Salvatore

E-Mail Website
Guest Editor
1. Department of Science, Technology and Society, University School for Advanced Studies IUSS Pavia, Palazzo del Broletto, Piazza della Vittoria 15, 27100 Pavia, Italy
2. DeepTrace Technologies, Milan, Italy
Interests: artificial intelligence; machine learning; pattern recognition; alzheimer's disease; neuroimaging; biomarkers; biomedical signal processing; predictive models, explainabel AI; brain-inspired AI
Dr. Petronilla Battista

E-Mail Website
Guest Editor
Laboratory of Neuropsychology, Clinical and Scientific Institute Maugeri IRCCS, 70124 Bari, Italy
Interests: cognitive, language, and communication disorders in patients with neurodegenerative disorders; frontotemporal dementia; Alzheimer’s disease
Prof. Dr. Juan Manuel Gorriz

E-Mail Website
Guest Editor
Department of Signal Theory, Telematics and Communications, University of Granada, 18071 Granada, Spain
Interests: neuroimaging; statistical learning theory; biomedical signal processing; computer-aided diagnosis
Special Issues, Collections and Topics in MDPI journals
Dr. Stavros I. Dimitriadis

E-Mail Website
Guest Editor
1. Department of Clinical Psychology and Psychobiology, University of Barcelona, Barcelona, Spain
2. Institut de Neurociències, University of Barcelona, Barcelona, Spain
3. Integrative Neuroimaging Lab, 55133 Thessaloniki, Macedonia, Greece
4. Neuroinformatics Group, Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff CF24 4HQ, UK
Interests: multimodal neuroimaging; genetic neuroimaging; network neuroscience; biomarkers; reproducibility in neuroscience and neuroimaging analysis; biomedical signal processing; artificial intelligence; machine learning; Alzheimer’s disease; schizophrenia; traumatic brain injury; intervention protocols
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has emerged as a cutting-edge computational tool for analyzing vast and complex datasets in various scientific fields. Its application in neuroscience has garnered significant attention, as AI possesses the capacity to unveil intricate patterns and nonlinear relationships within large and complex data.

This Special Issue, entitled "Neuroscience Meets Artificial Intelligence", focuses on the intersection of neuroscience and AI, aiming to explore the synergistic potential of combining these two disciplines to advance our understanding of the brain and develop innovative applications in these areas.

This potential is threefold: on the one hand, AI can be used to develop diagnostic, prognostic and predictive models able to support clinical decisions; belonging to this category, AI-based methodologies in the realm of medical diagnosis for neurological disorders can indeed improve the diagnostic and treatment process for degenerative diseases (i.e., Alzheimer’s disease, frontotemporal dementia, Parkinson’s disease); on the other hand, neuroscientific knowledge can be used to draw inspiration for brain-like AI systems, with the aim of improving their current performance, also in terms of required computational power; lastly, such systems could be used as human–brain models to verify hypotheses drawn by neuroscientists.

The Issue will feature cutting-edge research that brings together neuroscience principles and AI methodologies, fostering collaboration between the two disciplines. This Special Issue seeks to bridge the gap between neuroscience and AI, promoting interdisciplinary research and facilitating the translation of scientific discoveries into practical applications for the benefit of society.

The solicited papers will encompass a wide range of topics, including machine learning and deep learning, neural network modeling, brain-computer interface, AI applied to cognitive, linguistic, and computational neuroscience, autonomous intelligent systems inspired by the brain, and exploratory research on new emerging AI techniques. Authors are invited to submit relevant original research articles as well as opinion and review papers. The use of standard checklists for AI is appreciated.

Dr. Christian Salvatore
Dr. Petronilla Battista
Prof. Dr. Juan Manuel Gorriz
Dr. Stavros I. Dimitriadis
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-inspired AI
  • brain-computer interface
  • brain imaging
  • computer-aided diagnosis
  • explainable AI
  • neuroimaging
  • neurological disorders
  • neurodevelopmental disorders
  • neurological/biomedical signal processing
  • predictive models

Published Papers (3 papers)

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13 pages, 4328 KiB  
Article
Machine Learning-Assisted Classification of Paraffin-Embedded Brain Tumors with Raman Spectroscopy
by Gilbert Georg Klamminger, Laurent Mombaerts, Françoise Kemp, Finn Jelke, Karoline Klein, Rédouane Slimani, Giulia Mirizzi, Andreas Husch, Frank Hertel, Michel Mittelbronn and Felix B. Kleine Borgmann
Brain Sci. 2024, 14(4), 301; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci14040301 - 23 Mar 2024
Viewed by 656
Abstract
Raman spectroscopy (RS) has demonstrated its utility in neurooncological diagnostics, spanning from intraoperative tumor detection to the analysis of tissue samples peri- and postoperatively. In this study, we employed Raman spectroscopy (RS) to monitor alterations in the molecular vibrational characteristics of a broad [...] Read more.
Raman spectroscopy (RS) has demonstrated its utility in neurooncological diagnostics, spanning from intraoperative tumor detection to the analysis of tissue samples peri- and postoperatively. In this study, we employed Raman spectroscopy (RS) to monitor alterations in the molecular vibrational characteristics of a broad range of formalin-fixed, paraffin-embedded (FFPE) intracranial neoplasms (including primary brain tumors and meningiomas, as well as brain metastases) and considered specific challenges when employing RS on FFPE tissue during the routine neuropathological workflow. We spectroscopically measured 82 intracranial neoplasms on CaF2 slides (in total, 679 individual measurements) and set up a machine learning framework to classify spectral characteristics by splitting our data into training cohorts and external validation cohorts. The effectiveness of our machine learning algorithms was assessed by using common performance metrics such as AUROC and AUPR values. With our trained random forest algorithms, we distinguished among various types of gliomas and identified the primary origin in cases of brain metastases. Moreover, we spectroscopically diagnosed tumor types by using biopsy fragments of pure necrotic tissue, a task unattainable through conventional light microscopy. In order to address misclassifications and enhance the assessment of our models, we sought out significant Raman bands suitable for tumor identification. Through the validation phase, we affirmed a considerable complexity within the spectroscopic data, potentially arising not only from the biological tissue subjected to a rigorous chemical procedure but also from residual components of the fixation and paraffin-embedding process. The present study demonstrates not only the potential applications but also the constraints of RS as a diagnostic tool in neuropathology, considering the challenges associated with conducting vibrational spectroscopic analysis on formalin-fixed, paraffin-embedded (FFPE) tissue. Full article
(This article belongs to the Special Issue Neuroscience Meets Artificial Intelligence)
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20 pages, 2660 KiB  
Article
Sex Differences in Conversion Risk from Mild Cognitive Impairment to Alzheimer’s Disease: An Explainable Machine Learning Study with Random Survival Forests and SHAP
by Alessia Sarica, Assunta Pelagi, Federica Aracri, Fulvia Arcuri, Aldo Quattrone, Andrea Quattrone and for the Alzheimer’s Disease Neuroimaging Initiative
Brain Sci. 2024, 14(3), 201; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci14030201 - 22 Feb 2024
Viewed by 908
Abstract
Alzheimer’s disease (AD) exhibits sex-linked variations, with women having a higher prevalence, and little is known about the sexual dimorphism in progressing from Mild Cognitive Impairment (MCI) to AD. The main aim of our study was to shed light on the sex-specific conversion-to-AD [...] Read more.
Alzheimer’s disease (AD) exhibits sex-linked variations, with women having a higher prevalence, and little is known about the sexual dimorphism in progressing from Mild Cognitive Impairment (MCI) to AD. The main aim of our study was to shed light on the sex-specific conversion-to-AD risk factors using Random Survival Forests (RSF), a Machine Learning survival approach, and Shapley Additive Explanations (SHAP) on dementia biomarkers in stable (sMCI) and progressive (pMCI) patients. With this purpose, we built two separate models for male (M-RSF) and female (F-RSF) cohorts to assess whether global explanations differ between the sexes. Similarly, SHAP local explanations were obtained to investigate changes across sexes in feature contributions to individual risk predictions. The M-RSF achieved higher performance on the test set (0.87) than the F-RSF (0.79), and global explanations of male and female models had limited similarity (<71.1%). Common influential variables across the sexes included brain glucose metabolism and CSF biomarkers. Conversely, the M-RSF had a notable contribution from hippocampus, which had a lower impact on the F-RSF, while verbal memory and executive function were key contributors only in F-RSF. Our findings confirmed that females had a higher risk of progressing to dementia; moreover, we highlighted distinct sex-driven patterns of variable importance, uncovering different feature contribution risks across sexes that decrease/increase the conversion-to-AD risk. Full article
(This article belongs to the Special Issue Neuroscience Meets Artificial Intelligence)
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Other

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27 pages, 1780 KiB  
Systematic Review
Unveiling the Diagnostic Potential of Linguistic Markers in Identifying Individuals with Parkinson’s Disease through Artificial Intelligence: A Systematic Review
by Cinzia Palmirotta, Simona Aresta, Petronilla Battista, Serena Tagliente, Gianvito Lagravinese, Davide Mongelli, Christian Gelao, Pietro Fiore, Isabella Castiglioni, Brigida Minafra and Christian Salvatore
Brain Sci. 2024, 14(2), 137; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci14020137 - 28 Jan 2024
Viewed by 1183
Abstract
While extensive research has documented the cognitive changes associated with Parkinson’s disease (PD), a relatively small portion of the empirical literature investigated the language abilities of individuals with PD. Recently, artificial intelligence applied to linguistic data has shown promising results in predicting the [...] Read more.
While extensive research has documented the cognitive changes associated with Parkinson’s disease (PD), a relatively small portion of the empirical literature investigated the language abilities of individuals with PD. Recently, artificial intelligence applied to linguistic data has shown promising results in predicting the clinical diagnosis of neurodegenerative disorders, but a deeper investigation of the current literature available on PD is lacking. This systematic review investigates the nature of language disorders in PD by assessing the contribution of machine learning (ML) to the classification of patients with PD. A total of 10 studies published between 2016 and 2023 were included in this review. Tasks used to elicit language were mainly structured or unstructured narrative discourse. Transcriptions were mostly analyzed using Natural Language Processing (NLP) techniques. The classification accuracy (%) ranged from 43 to 94, sensitivity (%) ranged from 8 to 95, specificity (%) ranged from 3 to 100, AUC (%) ranged from 32 to 97. The most frequent optimal linguistic measures were lexico-semantic (40%), followed by NLP-extracted features (26%) and morphological consistency features (20%). Artificial intelligence applied to linguistic markers provides valuable insights into PD. However, analyzing measures derived from narrative discourse can be time-consuming, and utilizing ML requires specialized expertise. Moving forward, it is important to focus on facilitating the integration of both narrative discourse analysis and artificial intelligence into clinical practice. Full article
(This article belongs to the Special Issue Neuroscience Meets Artificial Intelligence)
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