Deep into the Brain: Artificial Intelligence in Brain Diseases—2nd Edition

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

Deadline for manuscript submissions: 31 March 2025 | Viewed by 1014

Special Issue Editors


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Department of Dynamic, Clinical Psychology and Health, Faculty of Medicine and Psychology, Sapienza University of Rome, 00185 Rome, Italy
Interests: clinical neuroscience; psychopathology; epigenetics; connectivity; neuroimaging
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Special Issue Information

Dear Colleagues,

Brain diseases (or neurological disorders) cause the disruption of normal functioning of the nervous system, where structural, biochemical or electrical abnormalities in the brain can result in a variety of symptoms. The expression “brain diseases” includes more than 600 disorders of the nervous system, such as epilepsy, dementia, Alzheimer’s disease and cerebrovascular diseases, including cerebral vascular accidents (CVAs), stroke, multiple sclerosis, Parkinson’s disease, migraine, neuroinfectious, brain tumours and traumatic disorders. According to the World Health Statistics 2020 published by the WHO, over ten million people have died from brain diseases yearly since 2016. The diagnosis and prevention of brain diseases represent a growing and among the most difficult challenges of modern medicine. The early detection of these disorders could make a significant impact in providing better prognosis and more adequate therapies, as well as appropriate resource utilisation. Different types of neurological disorders are characterised by specific alterations in brain structures and functions. In order to enhance our understanding of the brain mechanisms underlying these clinical conditions, medical imaging techniques such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and Positron Emission Tomography (PET) are usually employed. However, neuroimaging approaches return a significant amount of information where identifying the specific brain processes associated with the clinical condition of interest might be challenging. Additionally, the standard processing of medical imaging outcomes can be time-consuming and comes with a non-negligible chance of error. Artificial Intelligence (AI) techniques have a key role in automatizing those processes, leading to more accurate clinical assessments. AI has received growing interest in the field of medical imaging and computational neurosciences over the last decade. Specifically, Machine Learning (ML) and Deep Learning (DL) are widely used to address brain-related open issues, classify different clinical conditions and predict the onset of brain diseases.

This Special Issue aims to collect the latest works showing the successful employment of AI to enhance the investigation, diagnosis and outcome prediction of brain diseases. Areas covered by this section include, but are not limited to, the following:

  • Brain disease prevention;
  • Development and validation of AI algorithms;
  • Physio-physiological assessment;
  • Wearable technologies;
  • Neuroimaging in patients with brain disorders.

All types of manuscripts will be considered, including original basic science reports, translational research, clinical studies, review articles and methodology papers.

Dr. Gianluca Borghini
Dr. Pietro Aricò
Dr. Gaia Romana Pellicano
Dr. Alessandra Anzolin
Guest Editors

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. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • artificial intelligence
  • brain diseases
  • neurological disorders
  • machine learning
  • deep learning
  • neuroimaging
  • neuroscience
  • neurophysiological measures
  • mental states
  • multimodal approach

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Published Papers (1 paper)

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Research

21 pages, 2757 KiB  
Article
Classifying Unconscious, Psychedelic, and Neuropsychiatric Brain States with Functional Connectivity, Graph Theory, and Cortical Gradient Analysis
by Hyunwoo Jang, Rui Dai, George A. Mashour, Anthony G. Hudetz and Zirui Huang
Brain Sci. 2024, 14(9), 880; https://doi.org/10.3390/brainsci14090880 - 30 Aug 2024
Viewed by 840
Abstract
Accurate and generalizable classification of brain states is essential for understanding their neural underpinnings and improving clinical diagnostics. Traditionally, functional connectivity patterns and graph-theoretic metrics have been utilized. However, cortical gradient features, which reflect global brain organization, offer a complementary approach. We hypothesized [...] Read more.
Accurate and generalizable classification of brain states is essential for understanding their neural underpinnings and improving clinical diagnostics. Traditionally, functional connectivity patterns and graph-theoretic metrics have been utilized. However, cortical gradient features, which reflect global brain organization, offer a complementary approach. We hypothesized that a machine learning model integrating these three feature sets would effectively discriminate between baseline and atypical brain states across a wide spectrum of conditions, even though the underlying neural mechanisms vary. To test this, we extracted features from brain states associated with three meta-conditions including unconsciousness (NREM2 sleep, propofol deep sedation, and propofol general anesthesia), psychedelic states induced by hallucinogens (subanesthetic ketamine, lysergic acid diethylamide, and nitrous oxide), and neuropsychiatric disorders (attention-deficit hyperactivity disorder, bipolar disorder, and schizophrenia). We used support vector machine with nested cross-validation to construct our models. The soft voting ensemble model marked the average balanced accuracy (average of specificity and sensitivity) of 79% (62–98% across all conditions), outperforming individual base models (70–76%). Notably, our models exhibited varying degrees of transferability across different datasets, with performance being dependent on the specific brain states and feature sets used. Feature importance analysis across meta-conditions suggests that the underlying neural mechanisms vary significantly, necessitating tailored approaches for accurate classification of specific brain states. This finding underscores the value of our feature-integrated ensemble models, which leverage the strengths of multiple feature types to achieve robust performance across a broader range of brain states. While our approach offers valuable insights into the neural signatures of different brain states, future work is needed to develop and validate even more generalizable models that can accurately classify brain states across a wider array of conditions. Full article
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