Recent Advances in Neuroscience: Theoretical, Applied, and Experimental Research

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1644

Special Issue Editor


E-Mail Website
Guest Editor
Cognitive Neuroimaging Laboratory, Department of Biology, Montclair State University, 320 Science Hall, Montclair, NJ 07043, USA
Interests: neural correlates of higher-order cognition; single-pulse and repetitive transcranial magnetic stimulation; evolutionary cognitive neuroscience; self-awareness
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

  • Introduction: From the Egyptians and the Greeks to optogenetics and gene therapy, neuroscience has always been based in the future. In this Special Issue, we report the most recent findings using the most cutting-edge methods. Connectomes and CRISPR are the future, but so are tried-and-true methods such as the well-documented case study technique. In this issue, the best of neuroscience is encouraged. “Cutting edge” does not only refer to techniques; new theories spawn new ideas that open up the future.
  • Aim of this Special issue: We are soliciting the most advanced ideas and findings in neuroscience, generally speaking and broadly defined. Papers can be reviews of the literature or purely theoretical, but we will give preference to original experimental findings. While modern techniques fit our aims, classic methods with advanced findings are also encouraged. Authors are especially encouaged to submit papers that excite and advance. This issue is the perfect place for both studies that venture outside the typical and research that can move the knowledge base to a new area.

Suggested themes and article types for submissions include experimental findings, theortical manuscripts, and review papers. Clinical results are welcome, as well as those that are purely experimental. Papers from an evolutionary perspective that are novel in their findings or theory are encouraged. We look forward to receiving your contributions.

Prof. Dr. Julian Keenan
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. 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

  • neuroscience
  • genetics
  • brain
  • optogenetics
  • fMRI
  • MRI
  • TMS
  • tDCS
  • gene therapy
  • evolutionary neuroscience
  • psychiatric disorders

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

12 pages, 555 KiB  
Article
AI-Driven Information for Relatives of Patients with Malignant Middle Cerebral Artery Infarction: A Preliminary Validation Study Using GPT-4o
by Mejdeddine Al Barajraji, Sami Barrit, Nawfel Ben-Hamouda, Ethan Harel, Nathan Torcida, Beatrice Pizzarotti, Nicolas Massager and Jerome R. Lechien
Brain Sci. 2025, 15(4), 391; https://doi.org/10.3390/brainsci15040391 - 11 Apr 2025
Viewed by 378
Abstract
Purpose: This study examines GPT-4o’s ability to communicate effectively with relatives of patients undergoing decompressive hemicraniectomy (DHC) after malignant middle cerebral artery infarction (MMCAI). Methods: GPT-4o was asked 25 common questions from patients’ relatives about DHC for MMCAI, twice over a 7-day interval. [...] Read more.
Purpose: This study examines GPT-4o’s ability to communicate effectively with relatives of patients undergoing decompressive hemicraniectomy (DHC) after malignant middle cerebral artery infarction (MMCAI). Methods: GPT-4o was asked 25 common questions from patients’ relatives about DHC for MMCAI, twice over a 7-day interval. Responses were rated for accuracy, clarity, relevance, completeness, sourcing, and usefulness by board-certified intensivist* (one), neurologists, and neurosurgeons using the Quality Analysis of Medical AI (QAMAI) tool. Interrater reliability and stability were measured using ICC and Pearson’s correlation. Results: The total QAMAI scores were 22.32 ± 3.08 for the intensivist, 24.68 ± 2.8 for the neurologist, 23.36 ± 2.86 and 26.32 ± 2.91 for the neurosurgeons, representing moderate-to-high accuracy. The evaluators reported moderate ICC (0.631, 95% CI: 0.321–0.821). The highest subscores were for the categories of accuracy, clarity, and relevance while the poorest were associated with completeness, usefulness, and sourcing. GPT-4o did not systematically provide references for their responses. The stability analysis reported moderate-to-high stability. The readability assessment revealed an FRE score of 7.23, an FKG score of 15.87 and a GF index of 18.15. Conclusions: GPT-4o provides moderate-to-high quality information related to DHC for MMCAI, with strengths in accuracy, clarity, and relevance. However, limitations in completeness, sourcing, and readability may impact its effectiveness in patient or their relatives’ education. Full article
Show Figures

Figure 1

15 pages, 2597 KiB  
Article
Specialized Large Language Model Outperforms Neurologists at Complex Diagnosis in Blinded Case-Based Evaluation
by Sami Barrit, Nathan Torcida, Aurelien Mazeraud, Sebastien Boulogne, Jeanne Benoit, Timothée Carette, Thibault Carron, Bertil Delsaut, Eva Diab, Hugo Kermorvant, Adil Maarouf, Sofia Maldonado Slootjes, Sylvain Redon, Alexis Robin, Sofiene Hadidane, Vincent Harlay, Vito Tota, Tanguy Madec, Alexandre Niset, Mejdeddine Al Barajraji, Joseph R. Madsen, Salim El Hadwe, Nicolas Massager, Stanislas Lagarde and Romain Carronadd Show full author list remove Hide full author list
Brain Sci. 2025, 15(4), 347; https://doi.org/10.3390/brainsci15040347 - 27 Mar 2025
Cited by 1 | Viewed by 626
Abstract
Background/Objectives: Artificial intelligence (AI), particularly large language models (LLMs), has demonstrated versatility in various applications but faces challenges in specialized domains like neurology. This study evaluates a specialized LLM’s capability and trustworthiness in complex neurological diagnosis, comparing its performance to neurologists in [...] Read more.
Background/Objectives: Artificial intelligence (AI), particularly large language models (LLMs), has demonstrated versatility in various applications but faces challenges in specialized domains like neurology. This study evaluates a specialized LLM’s capability and trustworthiness in complex neurological diagnosis, comparing its performance to neurologists in simulated clinical settings. Methods: We deployed GPT-4 Turbo (OpenAI, San Francisco, CA, US) through Neura (Sciense, New York, NY, US), an AI infrastructure with a dual-database architecture integrating “long-term memory” and “short-term memory” components on a curated neurological corpus. Five representative clinical scenarios were presented to 13 neurologists and the AI system. Participants formulated differential diagnoses based on initial presentations, followed by definitive diagnoses after receiving conclusive clinical information. Two senior academic neurologists blindly evaluated all responses, while an independent investigator assessed the verifiability of AI-generated information. Results: AI achieved a significantly higher normalized score (86.17%) compared to neurologists (55.11%, p < 0.001). For differential diagnosis questions, AI scored 85% versus 46.15% for neurologists, and for final diagnosis, 88.24% versus 70.93%. AI obtained 15 maximum scores in its 20 evaluations and responded in under 30 s compared to neurologists’ average of 9 min. All AI-provided references were classified as relevant with no hallucinatory content detected. Conclusions: A specialized LLM demonstrated superior diagnostic performance compared to practicing neurologists across complex clinical challenges. This indicates that appropriately harnessed LLMs with curated knowledge bases can achieve domain-specific relevance in complex clinical disciplines, suggesting potential for AI as a time-efficient asset in clinical practice. Full article
Show Figures

Figure 1

Back to TopTop