Deep into the Brain: Artificial Intelligence in Brain Diseases

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 4001

Special Issue Editors

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

Special Issue Information

Dear Colleagues,

Brain diseases (or neurological disorders) cause the disruption of the 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 millions of people have died from brain diseases yearly since 2016. The diagnosis and prevention of brain diseases represent a growing and one of the most difficult challenges of modern medicine. Early detection of these disorders could make a wide 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 those 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 at collecting the latest works showing the successful employment of AI to enhance the investigation, diagnosis and outcome prediction of brain disease. 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 are 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

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

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

Published Papers (3 papers)

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

Research

Jump to: Review

17 pages, 944 KiB  
Article
Pareto Optimized Adaptive Learning with Transposed Convolution for Image Fusion Alzheimer’s Disease Classification
Brain Sci. 2023, 13(7), 1045; https://doi.org/10.3390/brainsci13071045 - 08 Jul 2023
Cited by 3 | Viewed by 1166
Abstract
Alzheimer’s disease (AD) is a neurological condition that gradually weakens the brain and impairs cognition and memory. Multimodal imaging techniques have become increasingly important in the diagnosis of AD because they can help monitor disease progression over time by providing a more complete [...] Read more.
Alzheimer’s disease (AD) is a neurological condition that gradually weakens the brain and impairs cognition and memory. Multimodal imaging techniques have become increasingly important in the diagnosis of AD because they can help monitor disease progression over time by providing a more complete picture of the changes in the brain that occur over time in AD. Medical image fusion is crucial in that it combines data from various image modalities into a single, better-understood output. The present study explores the feasibility of employing Pareto optimized deep learning methodologies to integrate Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) images through the utilization of pre-existing models, namely the Visual Geometry Group (VGG) 11, VGG16, and VGG19 architectures. Morphological operations are carried out on MRI and PET images using Analyze 14.0 software and after which PET images are manipulated for the desired angle of alignment with MRI image using GNU Image Manipulation Program (GIMP). To enhance the network’s performance, transposed convolution layer is incorporated into the previously extracted feature maps before image fusion. This process generates feature maps and fusion weights that facilitate the fusion process. This investigation concerns the assessment of the efficacy of three VGG models in capturing significant features from the MRI and PET data. The hyperparameters of the models are tuned using Pareto optimization. The models’ performance is evaluated on the ADNI dataset utilizing the Structure Similarity Index Method (SSIM), Peak Signal-to-Noise Ratio (PSNR), Mean-Square Error (MSE), and Entropy (E). Experimental results show that VGG19 outperforms VGG16 and VGG11 with an average of 0.668, 0.802, and 0.664 SSIM for CN, AD, and MCI stages from ADNI (MRI modality) respectively. Likewise, an average of 0.669, 0.815, and 0.660 SSIM for CN, AD, and MCI stages from ADNI (PET modality) respectively. Full article
(This article belongs to the Special Issue Deep into the Brain: Artificial Intelligence in Brain Diseases)
Show Figures

Figure 1

Review

Jump to: Research

15 pages, 278 KiB  
Review
The Clinical Relevance of Artificial Intelligence in Migraine
Brain Sci. 2024, 14(1), 85; https://doi.org/10.3390/brainsci14010085 - 16 Jan 2024
Viewed by 814
Abstract
Migraine is a burdensome neurological disorder that still lacks clear and easily accessible diagnostic biomarkers. Furthermore, a straightforward pathway is hard to find for migraineurs’ management, so the search for response predictors has become urgent. Nowadays, artificial intelligence (AI) has pervaded almost every [...] Read more.
Migraine is a burdensome neurological disorder that still lacks clear and easily accessible diagnostic biomarkers. Furthermore, a straightforward pathway is hard to find for migraineurs’ management, so the search for response predictors has become urgent. Nowadays, artificial intelligence (AI) has pervaded almost every aspect of our lives, and medicine has not been missed. Its applications are nearly limitless, and the ability to use machine learning approaches has given researchers a chance to give huge amounts of data new insights. When it comes to migraine, AI may play a fundamental role, helping clinicians and patients in many ways. For example, AI-based models can increase diagnostic accuracy, especially for non-headache specialists, and may help in correctly classifying the different groups of patients. Moreover, AI models analysing brain imaging studies reveal promising results in identifying disease biomarkers. Regarding migraine management, AI applications showed value in identifying outcome measures, the best treatment choices, and therapy response prediction. In the present review, the authors introduce the various and most recent clinical applications of AI regarding migraine. Full article
(This article belongs to the Special Issue Deep into the Brain: Artificial Intelligence in Brain Diseases)
30 pages, 2358 KiB  
Review
The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook
Brain Sci. 2023, 13(10), 1462; https://doi.org/10.3390/brainsci13101462 - 16 Oct 2023
Cited by 1 | Viewed by 1339
Abstract
Neurological disorders (NDs), such as Alzheimer’s disease, have been a threat to human health all over the world. It is of great importance to diagnose ND through combining artificial intelligence technology and brain imaging. A graph neural network (GNN) can model and analyze [...] Read more.
Neurological disorders (NDs), such as Alzheimer’s disease, have been a threat to human health all over the world. It is of great importance to diagnose ND through combining artificial intelligence technology and brain imaging. A graph neural network (GNN) can model and analyze the brain, imaging from morphology, anatomical structure, function features, and other aspects, thus becoming one of the best deep learning models in the diagnosis of ND. Some researchers have investigated the application of GNN in the medical field, but the scope is broad, and its application to NDs is less frequent and not detailed enough. This review focuses on the research progress of GNNs in the diagnosis of ND. Firstly, we systematically investigated the GNN framework of ND, including graph construction, graph convolution, graph pooling, and graph prediction. Secondly, we investigated common NDs using the GNN diagnostic model in terms of data modality, number of subjects, and diagnostic accuracy. Thirdly, we discussed some research challenges and future research directions. The results of this review may be a valuable contribution to the ongoing intersection of artificial intelligence technology and brain imaging. Full article
(This article belongs to the Special Issue Deep into the Brain: Artificial Intelligence in Brain Diseases)
Show Figures

Figure 1

Back to TopTop