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Special Issue "Advances of AI in Neuroimaging"

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

Deadline for manuscript submissions: 31 March 2024 | Viewed by 2814

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Special Issue Editors

Dr. Iman Beheshti

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Guest Editor

Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB, Canada
Interests: neuroimaging; advanced machine learning

Dr. Daichi Sone

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Co-Guest Editor

Department of Psychiatry, Jikei University School of Medicine, Tokyo, Japan
Interests: neuroimaging; brain health; epilepsy; Alzheimer diseases
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Carson K. Leung

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Co-Guest Editor

Department of Computer Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
Interests: AI; data mining and analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Neuroimaging is a rapidly evolving field that involves the use of non-invasive imaging techniques to visualize and study the structure and function of the human brain. With the advent of artificial intelligence (AI), the field of neuroimaging has seen significant breakthroughs in terms of accuracy, speed, and efficiency in identifying various brain disorders. AI models have been widely applied in the analysis and interpretation of neuroimaging data, aiding researchers and clinicians to diagnose, treat, and monitor patients with neurological and psychiatric disorders. The aim of this research topic is to present advanced AI methods for application in neuroimaging techniques such as magnetic resonance imaging, positron emission tomography, and computed tomography. We are interested in understanding how AI models, coupled with neuroimaging, can advance our understanding of the human brain, its functions, and the mechanisms of brain diseases. We are also keen to know how AI methods in neuroimaging can be used in diagnosis, the improvement of patient care, cost reduction, the enhancement of clinical decision making, as well as the treatment and monitoring of patients with neurological and psychiatric disorders. In this research topic, we welcome original research papers or high-quality manuscripts focusing on the applications of AI methods in neuroimaging. Potential topics include, but are not limited to:

AI methods for brain diagnosis and diseases outcome;
AI in brain abnormality segmentations;
Interpreting machine-learning models in neuroimaging;
AI models in neurofeedback;
AI models for neuroimaging-based biomarker discovery;
Neuroimaging analysis.

Dr. Iman Beheshti
Dr. Daichi Sone
Prof. Dr. Carson Leung
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

AI
machine learning
deep learning
neuroimaging
brain

Published Papers (3 papers)

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Research

12 pages, 1488 KiB

Open AccessArticle

Automatic Diagnosis of Major Depressive Disorder Using a High- and Low-Frequency Feature Fusion Framework

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Brain Sci. 2023, 13(11), 1590; https://doi.org/10.3390/brainsci13111590 - 15 Nov 2023

Viewed by 550

Abstract

Major Depressive Disorder (MDD) is a common mental illness resulting in immune disorders and even thoughts of suicidal behavior. Neuroimaging techniques serve as a quantitative tool for the assessment of MDD diagnosis. In the domain of computer-aided magnetic resonance imaging diagnosis, current research predominantly focuses on isolated local or global information, often neglecting the synergistic integration of multiple data sources, thus potentially overlooking valuable details. To address this issue, we proposed a diagnostic model for MDD that integrates high-frequency and low-frequency information using data from diffusion tensor imaging (DTI), structural magnetic resonance imaging (sMRI), and functional magnetic resonance imaging (fMRI). First, we designed a meta-low-frequency encoder (MLFE) and a meta-high-frequency encoder (MHFE) to extract the low-frequency and high-frequency feature information from DTI and sMRI, respectively. Then, we utilized a multilayer perceptron (MLP) to extract features from fMRI data. Following the feature cross-fusion, we designed the ensemble learning threshold voting method to determine the ultimate diagnosis for MDD. The model achieved accuracy, precision, specificity, F1-score, MCC, and AUC values of 0.724, 0.750, 0.882, 0.600, 0.421, and 0.667, respectively. This approach provides new research ideas for the diagnosis of MDD. Full article

(This article belongs to the Special Issue Advances of AI in Neuroimaging)

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11 pages, 1555 KiB

Open AccessArticle

Auto-Classification of Parkinson’s Disease with Different Motor Subtypes Using Arterial Spin Labelling MRI Based on Machine Learning

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Brain Sci. 2023, 13(11), 1524; https://doi.org/10.3390/brainsci13111524 - 29 Oct 2023

Viewed by 612

Abstract

The purpose of this study was to automatically classify different motor subtypes of Parkinson’s disease (PD) on arterial spin labelling magnetic resonance imaging (ASL-MRI) data using support vector machine (SVM). This study included 38 subjects: 21 PD patients and 17 normal controls (NCs). Based on the Unified Parkinson’s Disease Rating Scale (UPDRS) subscores, patients were divided into the tremor-dominant (TD) subtype and the postural instability gait difficulty (PIGD) subtype. The subjects were in a resting state during the acquisition of ASL-MRI data. The automated anatomical atlas 3 (AAL3) template was registered to obtain an ASL image of the same size and shape. We obtained the voxel values of 170 brain regions by considering the location coordinates of these regions and then normalized the data. The length of the feature vector depended on the number of voxel values in each brain region. Three binary classification models were utilized for classifying subjects’ data, and we applied SVM to classify voxels in the brain regions. The left subgenual anterior cingulate cortex (ACC_sub_L) was clearly distinguished in both NCs and PD patients using SVM, and we obtained satisfactory diagnostic rates (accuracy = 92.31%, specificity = 96.97%, sensitivity = 84.21%, and AUCmax = 0.9585). For the right supramarginal gyrus (SupraMarginal_R), SVM distinguished the TD group from the other groups with satisfactory diagnostic rates (accuracy = 84.21%, sensitivity = 63.64%, specificity = 92.59%, and AUCmax = 0.9192). For the right intralaminar of thalamus (Thal_IL_R), SVM distinguished the PIGD group from the other groups with satisfactory diagnostic rates (accuracy = 89.47%, sensitivity = 70.00%, specificity = 6.43%, and AUCmax = 0.9464). These results are consistent with the changes in blood perfusion related to PD subtypes. In addition, the sensitive brain regions of the TD group and PIGD group involve the brain regions where the cerebellothalamocortical (CTC) and the striatal thalamocortical (STC) loops are located. Therefore, it is suggested that the blood perfusion patterns of the two loops may be different. These characteristic brain regions could become potential imaging markers of cerebral blood flow to distinguish TD from PIGD. Meanwhile, our findings provide an imaging basis for personalised treatment, thereby optimising clinical diagnostic and treatment approaches. Full article

(This article belongs to the Special Issue Advances of AI in Neuroimaging)

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22 pages, 3602 KiB

Open AccessArticle

Brain Tumor Classification from MRI Using Image Enhancement and Convolutional Neural Network Techniques

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Brain Sci. 2023, 13(9), 1320; https://doi.org/10.3390/brainsci13091320 - 14 Sep 2023

Cited by 1 | Viewed by 1010

Abstract

The independent detection and classification of brain malignancies using magnetic resonance imaging (MRI) can present challenges and the potential for error due to the intricate nature and time-consuming process involved. The complexity of the brain tumor identification process primarily stems from the need for a comprehensive evaluation spanning multiple modules. The advancement of deep learning (DL) has facilitated the emergence of automated medical image processing and diagnostics solutions, thereby offering a potential resolution to this issue. Convolutional neural networks (CNNs) represent a prominent methodology in visual learning and image categorization. The present study introduces a novel methodology integrating image enhancement techniques, specifically, Gaussian-blur-based sharpening and Adaptive Histogram Equalization using CLAHE, with the proposed model. This approach aims to effectively classify different categories of brain tumors, including glioma, meningioma, and pituitary tumor, as well as cases without tumors. The algorithm underwent comprehensive testing using benchmarked data from the published literature, and the results were compared with pre-trained models, including VGG16, ResNet50, VGG19, InceptionV3, and MobileNetV2. The experimental findings of the proposed method demonstrated a noteworthy classification accuracy of 97.84%, a precision success rate of 97.85%, a recall rate of 97.85%, and an F1-score of 97.90%. The results presented in this study showcase the exceptional accuracy of the proposed methodology in accurately classifying the most commonly occurring brain tumor types. The technique exhibited commendable generalization properties, rendering it a valuable asset in medicine for aiding physicians in making precise and proficient brain diagnoses. Full article

(This article belongs to the Special Issue Advances of AI in Neuroimaging)

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