Artificial Intelligence-based Computer-aided Diagnosis Applications for Brain Disorders from Medical Imaging Data, Volume II

Editorial

12 July 2023

Editorial: Artificial intelligence-based computer-aided diagnosis applications for brain disorders from medical imaging data, volume II

Fahmi Khalifa

Ahmed Shalaby

Ahmed Soliman

Safa Elaskary

, 1 more and

Mohamed Abdelazim

1,676 views

0 citations

Editors

Ahmed Shalaby

University of Texas Southwestern Medical Center

Fahmi Khalifa

Morgan State University

Ahmed Soliman

University of Louisville

Ahmed Refaey Hussein

Manhattan College

Mohamed Abdelazim

Department of Electrical Engineering, Faculty of Engineering, Mansoura University

Safa Elaskary

King Abdulaziz University

Impact

Pressure contour map for sVAD with VAH arteries and contralateral normal VAs at peak systole and late diastole. (A,B) Contralateral healthy VAs. (C,D) Steno-occlusive sVAD with VAH arteries. (E,F) Aneurysmal dilatative sVAD with VAH artery. →: dissected region. sVAD, spontaneous vertebral artery dissection; VAH, vertebral artery hypoplasia; VAs, vertebral arteries.

Original Research

14 June 2023

Abnormal flow pattern of low wall shear stress and high oscillatory shear index in spontaneous vertebral artery dissection with vertebral artery hypoplasia

Jiajia Bao

, 6 more and

Li He

1,898 views

3 citations

(A,B) Calibration curves of the radiomics and combined model on the testing cohort and training cohort, respectively.

Original Research

11 April 2023

Preoperative prediction of sonic hedgehog and group 4 molecular subtypes of pediatric medulloblastoma based on radiomics of multiparametric MRI combined with clinical parameters

Yuanlin Wang

, 8 more and

Yuting Zhang

2,014 views

5 citations

Classification between DS and NDS based on GMV. (A) Classification plot for training on DS/NDS data and testing on DS/NDS data using GMV. (B) ROC curve obtained by classifying DS and NDS using an SVM classifier. DS, deficit schizophrenia; NDS, non-deficit schizophrenia; GMV, gray matter volume; ROC, receiver operating characteristic curve; SVM, support vector machine.

Original Research

27 March 2023

Using support vector machine to explore the difference of function connection between deficit and non-deficit schizophrenia based on gray matter volume

Wenjing Zhu

, 3 more and

Zhijun Zhang

2,245 views

1 citations

Flow chart of research methods. Collect EEG generated by auditory oddball stimulation in three groups of subjects, and then select 23 channels of interest for brain network analysis and CNN classification.

Original Research

24 March 2023

EEG assessment of brain dysfunction for patients with chronic primary pain and depression under auditory oddball task

Yunzhe Li

, 5 more and

Shu Zhou

3,369 views

4 citations

$Variable importance. The bar-plot is color-coded to group MRI modalities from the same biophysical source. The striped block highlights the features contributing to 50% of the classification process. The y-axis is shared between columns. PD, proton density; intra, intra-neurite volume fraction; diff, intrinsic diffusivity; entr, orientation dispersion entropy; TSC, total sodium concentration; vol, tissue volume; WM, white matter; NAWM, normal appearing white matter; cGM, cortical gray matter; dGM, deep gray matter; les, lesions; HC, healthy controls; CIS, clinically isolated syndrome; RR, relapsing-remitting MS; SP, secondary progressive MS; MS, RR and SP. Continuing to Figure 4.$

Original Research

23 March 2023

Patterns of inflammation, microstructural alterations, and sodium accumulation define multiple sclerosis subtypes after 15 years from onset

Antonio Ricciardi

, 10 more and

Claudia A. M. Gandini Wheeler-Kingshott

2,129 views

0 citations

Each 3D image is decomposed into groups of 2D slices at equal intervals. In the first stage, deep convolutional features are extracted from each group by 2D CNN. In the second stage, all features from each slice group are concatenated to form a compact feature vector, and fed to EBLS for final prediction. (A) The overall architecture of BLADNet. (B) The detailed structure of EBLS.

Original Research

13 March 2023

Broad learning for early diagnosis of Alzheimer's disease using FDG-PET of the brain

Junwei Duan

, 4 more and

C. L. Philip Chen

6,641 views

11 citations

Original Research

07 March 2023

Disrupted interhemispheric coordination of sensory-motor networks and insula in major depressive disorder

Chunguo Zhang

, 11 more and

Wenbin Guo

Support vector machine (SVM) results. (A) The accuracy of classification of six SVM analyses. One represents the VMHC values in the cerebellum 8/vermis 8/vermis 9; 2 represents the VMHC values in the superior/middle occipital gyrus; 3 represents the VMHC values in the insula; 4 represents the combination of the VMHC values in the cerebellum 8/vermis 8/vermis 9 and superior/middle occipital gyrus; 5 represents the combination of the VMHC values in the cerebellum 8/vermis 8/vermis 9 and insula; and 6 represents the combination of the VMHC values in the superior/middle occipital gyrus and insula. (B) SVM analysis applied the combination of the VMHC values in the cerebellum 8/vermis 8/vermis 9 and insula. Sensitivity = 80.95%, specificity = 83.33%, and accuracy = 82.14%. In the left part, the red cross represents patient with MDD, and the green asterisk represents healthy controls. The blue circle represents support vector. (C) SVM analysis applied the combination of the VMHC values in the superior/middle occipital gyrus and insula. Sensitivity = 78.57%, specificity = 85.71%, and accuracy = 82.14%. In the left part, the red cross represents patient with MDD, and the green asterisk represents healthy controls. The blue circle represents support vector. SVM, support vector machines; VMHC, voxel-mirrored homotopic connectivity; MDD, major depressive disorder.

Objective: Prior researches have identified distinct differences in neuroimaging characteristics between healthy controls (HCs) and patients with major depressive disorder (MDD). However, the correlations between homotopic connectivity and clinical characteristics in patients with MDD have yet to be fully understood. The present study aimed to investigate common and unique patterns of homotopic connectivity and their relationships with clinical characteristics in patients with MDD.

Methods: We recruited 42 patients diagnosed with MDD and 42 HCs. We collected a range of clinical variables, as well as exploratory eye movement (EEM), event-related potentials (ERPs) and resting-state functional magnetic resonance imaging (rs-fMRI) data. The data were analyzed using correlation analysis, support vector machine (SVM), and voxel-mirrored homotopic connectivity (VMHC).

Results: Compared with HCs, patients with MDD showed decreased VMHC in the insula, and increased VMHC in the cerebellum 8/vermis 8/vermis 9 and superior/middle occipital gyrus. SVM analysis using VMHC values in the cerebellum 8/vermis 8/vermis 9 and insula, or VMHC values in the superior/middle occipital gyrus and insula as inputs can distinguish HCs and patients with MDD with high accuracy, sensitivity, and specificity.

Conclusion: The study demonstrated that decreased VMHC in the insula and increased VMHC values in the sensory-motor networks may be a distinctive neurobiological feature for patients with MDD, which could potentially serve as imaging markers to discriminate HCs and patients with MDD.

2,632 views

14 citations