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Brain Sciences
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Deep into the Brain: Artificial Intelligence in Brain Diseases
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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: closed (30 June 2024) | Viewed by 16697

Special Issue Editors

Dr. Gianluca Borghini

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Guest Editor
Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
Interests: cognitive neuroscience; machine learning; neuroscience; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Pietro Aricò

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Guest Editor
Department of Computer, Control, and Management Engineering "Antonio Ruberti", “Sapienza” University of Rome, 00185 Rome, Italy
Interests: brain-computer interface; neuroscience; signal-processing; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Gaia Romana Pellicano

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Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Dr. Alessandra Anzolin

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Guest Editor
Spaulding Rehabilitation Hospital, Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA 02115, USA
Interests: brain networks; hyperscanning; signal-processing
Special Issues, Collections and Topics in MDPI journals

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

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Published Papers (7 papers)

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Research

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16 pages, 1822 KiB  
Article
A Machine Learning Approach to Classifying EEG Data Collected with or without Haptic Feedback during a Simulated Drilling Task
by Michael S. Ramirez Campos, Heather S. McCracken, Alvaro Uribe-Quevedo, Brianna L. Grant, Paul C. Yielder and Bernadette A. Murphy
Brain Sci. 2024, 14(9), 894; https://doi.org/10.3390/brainsci14090894 - 31 Aug 2024
Viewed by 982
Abstract
Artificial Intelligence (AI), computer simulations, and virtual reality (VR) are increasingly becoming accessible tools that can be leveraged to implement training protocols and educational resources. Typical assessment tools related to sensory and neural processing associated with task performance in virtual environments often rely [...] Read more.
Artificial Intelligence (AI), computer simulations, and virtual reality (VR) are increasingly becoming accessible tools that can be leveraged to implement training protocols and educational resources. Typical assessment tools related to sensory and neural processing associated with task performance in virtual environments often rely on self-reported surveys, unlike electroencephalography (EEG), which is often used to compare the effects of different types of sensory feedback (e.g., auditory, visual, and haptic) in simulation environments in an objective manner. However, it can be challenging to know which aspects of the EEG signal represent the impact of different types of sensory feedback on neural processing. Machine learning approaches offer a promising direction for identifying EEG signal features that differentiate the impact of different types of sensory feedback during simulation training. For the current study, machine learning techniques were applied to differentiate neural circuitry associated with haptic and non-haptic feedback in a simulated drilling task. Nine EEG channels were selected and analyzed, extracting different time-domain, frequency-domain, and nonlinear features, where 360 features were tested (40 features per channel). A feature selection stage identified the most relevant features, including the Hurst exponent of 13–21 Hz, kurtosis of 21–30 Hz, power spectral density of 21–30 Hz, variance of 21–30 Hz, and spectral entropy of 13–21 Hz. Using those five features, trials with haptic feedback were correctly identified from those without haptic feedback with an accuracy exceeding 90%, increasing to 99% when using 10 features. These results show promise for the future application of machine learning approaches to predict the impact of haptic feedback on neural processing during VR protocols involving drilling tasks, which can inform future applications of VR and simulation for occupational skill acquisition. Full article
(This article belongs to the Special Issue Deep into the Brain: Artificial Intelligence in Brain Diseases)
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14 pages, 2462 KiB  
Article
Individual Deviation-Based Functional Hypergraph for Identifying Subtypes of Autism Spectrum Disorder
by Jialong Li, Weihao Zheng, Xiang Fu, Yu Zhang, Songyu Yang, Ying Wang, Zhe Zhang, Bin Hu and Guojun Xu
Brain Sci. 2024, 14(8), 738; https://doi.org/10.3390/brainsci14080738 - 24 Jul 2024
Viewed by 1151
Abstract
Heterogeneity has been one of the main barriers to understanding and treatment of autism spectrum disorder (ASD). Previous studies have identified several subtypes of ASD through unsupervised clustering analysis. However, most of them primarily depicted the pairwise similarity between individuals through second-order relationships, [...] Read more.
Heterogeneity has been one of the main barriers to understanding and treatment of autism spectrum disorder (ASD). Previous studies have identified several subtypes of ASD through unsupervised clustering analysis. However, most of them primarily depicted the pairwise similarity between individuals through second-order relationships, relying solely on patient data for their calculation. This leads to an underestimation of the complexity inherent in inter-individual relationships and the diagnostic information provided by typical development (TD). To address this, we utilized an elastic net model to construct an individual deviation-based hypergraph (ID-Hypergraph) based on functional MRI data. We then conducted a novel community detection clustering algorithm to the ID-Hypergraph, with the aim of identifying subtypes of ASD. By applying this framework to the Autism Brain Imaging Data Exchange repository data (discovery: 147/125, ASD/TD; replication: 134/132, ASD/TD), we identified four reproducible ASD subtypes with roughly similar patterns of ALFF between the discovery and replication datasets. Moreover, these subtypes significantly varied in communication domains. In addition, we achieved over 80% accuracy for the classification between these subtypes. Taken together, our study demonstrated the effectiveness of identifying subtypes of ASD through the ID-hypergraph, highlighting its potential in elucidating the heterogeneity of ASD and diagnosing ASD subtypes. Full article
(This article belongs to the Special Issue Deep into the Brain: Artificial Intelligence in Brain Diseases)
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27 pages, 5230 KiB  
Article
A Comparative Study on Feature Extraction Techniques for the Discrimination of Frontotemporal Dementia and Alzheimer’s Disease with Electroencephalography in Resting-State Adults
by Utkarsh Lal, Arjun Vinayak Chikkankod and Luca Longo
Brain Sci. 2024, 14(4), 335; https://doi.org/10.3390/brainsci14040335 - 29 Mar 2024
Cited by 5 | Viewed by 1873
Abstract
Early-stage Alzheimer’s disease (AD) and frontotemporal dementia (FTD) share similar symptoms, complicating their diagnosis and the development of specific treatment strategies. Our study evaluated multiple feature extraction techniques for identifying AD and FTD biomarkers from electroencephalographic (EEG) signals. We developed an optimised machine [...] Read more.
Early-stage Alzheimer’s disease (AD) and frontotemporal dementia (FTD) share similar symptoms, complicating their diagnosis and the development of specific treatment strategies. Our study evaluated multiple feature extraction techniques for identifying AD and FTD biomarkers from electroencephalographic (EEG) signals. We developed an optimised machine learning architecture that integrates sliding windowing, feature extraction, and supervised learning to distinguish between AD and FTD patients, as well as from healthy controls (HCs). Our model, with a 90% overlap for sliding windowing, SVD entropy for feature extraction, and K-Nearest Neighbors (KNN) for supervised learning, achieved a mean F1-score and accuracy of 93% and 91%, 92.5% and 93%, and 91.5% and 91% for discriminating AD and HC, FTD and HC, and AD and FTD, respectively. The feature importance array, an explainable AI feature, highlighted the brain lobes that contributed to identifying and distinguishing AD and FTD biomarkers. This research introduces a novel framework for detecting and discriminating AD and FTD using EEG signals, addressing the need for accurate early-stage diagnostics. Furthermore, a comparative evaluation of sliding windowing, multiple feature extraction, and machine learning methods on AD/FTD detection and discrimination is documented. Full article
(This article belongs to the Special Issue Deep into the Brain: Artificial Intelligence in Brain Diseases)
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20 pages, 1104 KiB  
Article
The Optimization of a Natural Language Processing Approach for the Automatic Detection of Alzheimer’s Disease Using GPT Embeddings
by Benjamin S. Runde, Ajit Alapati and Nicolas G. Bazan
Brain Sci. 2024, 14(3), 211; https://doi.org/10.3390/brainsci14030211 - 25 Feb 2024
Cited by 4 | Viewed by 3428
Abstract
The development of noninvasive and cost-effective methods of detecting Alzheimer’s disease (AD) is essential for its early prevention and mitigation. We optimize the detection of AD using natural language processing (NLP) of spontaneous speech through the use of audio enhancement techniques and novel [...] Read more.
The development of noninvasive and cost-effective methods of detecting Alzheimer’s disease (AD) is essential for its early prevention and mitigation. We optimize the detection of AD using natural language processing (NLP) of spontaneous speech through the use of audio enhancement techniques and novel transcription methodologies. Specifically, we utilized Boll Spectral Subtraction to improve audio fidelity and created transcriptions using state-of-the-art AI services—locally-based Wav2Vec and Whisper, alongside cloud-based IBM Cloud and Rev AI—evaluating their performance against traditional manual transcription methods. Support Vector Machine (SVM) classifiers were then trained and tested using GPT-based embeddings of transcriptions. Our findings revealed that AI-based transcriptions largely outperformed traditional manual ones, with Wav2Vec (enhanced audio) achieving the best accuracy and F-1 score (0.99 for both metrics) for locally-based systems and Rev AI (standard audio) performing the best for cloud-based systems (0.96 for both metrics). Furthermore, this study revealed the detrimental effects of interviewer speech on model performance in addition to the minimal effect of audio enhancement. Based on our findings, current AI transcription and NLP technologies are highly effective at accurately detecting AD with available data but struggle to classify probable AD and mild cognitive impairment (MCI), a prodromal stage of AD, due to a lack of training data, laying the groundwork for the future implementation of an automatic AD detection system. Full article
(This article belongs to the Special Issue Deep into the Brain: Artificial Intelligence in Brain Diseases)
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17 pages, 944 KiB  
Article
Pareto Optimized Adaptive Learning with Transposed Convolution for Image Fusion Alzheimer’s Disease Classification
by Modupe Odusami, Rytis Maskeliūnas and Robertas Damaševičius
Brain Sci. 2023, 13(7), 1045; https://doi.org/10.3390/brainsci13071045 - 8 Jul 2023
Cited by 9 | Viewed by 1839
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)
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Review

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15 pages, 278 KiB  
Review
The Clinical Relevance of Artificial Intelligence in Migraine
by Angelo Torrente, Simona Maccora, Francesco Prinzi, Paolo Alonge, Laura Pilati, Antonino Lupica, Vincenzo Di Stefano, Cecilia Camarda, Salvatore Vitabile and Filippo Brighina
Brain Sci. 2024, 14(1), 85; https://doi.org/10.3390/brainsci14010085 - 16 Jan 2024
Cited by 7 | Viewed by 2648
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
by Shuoyan Zhang, Jiacheng Yang, Ying Zhang, Jiayi Zhong, Wenjing Hu, Chenyang Li and Jiehui Jiang
Brain Sci. 2023, 13(10), 1462; https://doi.org/10.3390/brainsci13101462 - 16 Oct 2023
Cited by 4 | Viewed by 3159
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)
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