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Abstract Introduction The challenge of treating Glioblastoma (GBM) tumors is due to various mechanisms that make the tumor resistant to radiation therapy. One of these mechanisms is hypoxia, and therefore, determining the level of hypoxia can improve treatment planning and initial evaluation of its effectiveness in GBM. This study aimed to design an intelligent system to classify glioblastoma patients based on hypoxia levels obtained from magnetic resonance images with the help of an artificial neural network (ANN). Material and method MR images and PET measurements were available for this study. MR images were downloaded from the Cancer Imaging Archive (TCIA) database to classify glioblastoma patients based on hypoxia. The images in this database were prepared from 27 patients with glioblastoma on T1W + Gd, T2W-FLAIR, and T2W. Our designed algorithm includes various parts of pre-processing, tumor segmentation, feature extraction from images, and matching these features with quantitative parameters related to hypoxia in PET images. The system’s performance is evaluated by categorizing glioblastoma patients based on hypoxia. Results The results of classification with the artificial neural network (ANN) algorithm were as follows: the highest sensitivity, specificity, and accuracy were obtained at 86.71, 85.99 and 83.17%, respectively. The best specificity was related to the T2W-EDEMA image with the tumor to blood ratio (TBR) as a hypoxia parameter. T1W-NECROSIS image with the TBR parameter also showed the highest sensitivity and accuracy. Conclusion The results of the present study can be used in clinical procedures before treating glioblastoma patients. Among these treatment approaches, we can mention the radiotherapy treatment design and the prescription of effective drugs for the treatment of hypoxic tumors.

ABSTRACT Introduction Strong indications support the notion that idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) acts as a precursor to multiple α‐synucleinopathies, including Parkinson's disease and dementia with Lewy bodies. Despite numerous investigations into the alterations in cortical thickness and the volume of subcortical areas associated with this condition, comprehensive studies on the cortical surface morphology, focusing on gyrification and sulcal depth changes, are scarce. The purpose of this research was to explore the cortical surface morphology in individuals with probable iRBD (piRBD), to pinpoint early‐phase diagnostic markers. Methods This study included 30 piRBD patients confirmed using the RBD Screening Questionnaire (RBDSQ) and 33 control individuals selected from the Parkinson's Progression Markers Initiative (PPMI) database. They underwent neurophysiological tests and MRI scans. The FreeSurfer software was utilized to estimate cortical thickness (CTH), cortical and subcortical volumetry, local gyrification index (LGI), and sulcus depth (SD). Subsequently, these parameters were compared between the two groups. Additionally, linear correlation analysis was employed to estimate the relationship between brain morphological parameters and clinical parameters. Results Compared to the healthy control (HC), piRBD patients exhibited a significant reduction in CTH, LGI, and cortical volume in the bilateral superior parietal, lateral occipital, orbitofrontal, temporo‐occipital, bilateral rostral middle frontal, inferior parietal, and precentral brain regions. Moreover, a significant and notable correlation was observed between CTH and Geriatric Depression Scale (GDS), letter–number sequencing (LTNS), the Benton Judgment of Line Orientation (BJLO) test, and the symbol digit modalities test (SDMT) in several brain regions encompassing the motor cortex. Conclusion Patients with piRBD displayed widespread atrophy in various brain regions, predominantly covering the motor and sensory cortex. Furthermore, LGI could serve as a prognostic biomarker of disease's progression in piRBD.

Abstract Rapid eye movement behavior disorder (RBD) is a parasomnia characterized by the loss of skeletal muscle atonia during the rapid eye movement (REM) sleep phase. On the other hand, idiopathic RDB (iRBD) is considered the prelude of the various α‐synucleinopathies, including Parkinson's disease (PD), dementia with Lewy bodies and multiple system atrophy. Consequently, over 40% of patients eventually develop PD. Recent neuroimaging studies utilizing structural magnetic resonance imaging (s‐MRI), diffusion‐weighted imaging (DWI), and functional magnetic resonance imaging (fMRI) with graph theoretical analysis have demonstrated that patients with iRBD and Parkinson's disease have extensive brain abnormalities. Thus, it is crucial to identify new biomarkers that aid in determining the underlying physiopathology of iRBD group. This review was conducted systematically on the included full‐text articles of s‐MRI, DWI, and fMRI studies using graph theoretical analysis on patients with iRBD, per the procedures recommended by Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA). The literature search was conducted through the PubMed and Google scholar databases concentrating on studies from September to January 2022. Based on the three perspectives of integration, segregation, and centrality, the reviewed articles demonstrated that iRBD is associated with segregation disorders in frontal and limbic brain regions. Moreover, this study highlighted the need for additional longitudinal and multicenter studies to better understand the potential of graph metrics as brain biomarkers for identifying the underlying physiopathology of iRBD group.

Abstract Background Autism spectrum disorder (ASD) is a group of developmental disorders of the nervous system. Since the core cause of many of the symptoms of autism spectrum disorder is due to changes in the structure of the brain, the importance of examining the structural abnormalities of the brain in these disorder becomes apparent. The aim of this study is evaluation of brain structure abnormalities in children with autism spectrum disorder (ASD) using structural magnetic resonance imaging (sMRI). sMRI images of 26 autistic and 26 Healthy control subjects in the range of 5–10 years are selected from the ABIDE database. For a better assessment of structural abnormalities, the surface and volume features are extracted together from this images. Then, the extracted features from both groups were compared with the sample t test and the features with significant differences between the two groups were identified. Results The results of volume-based features indicate an increase in total brain volume and white matter and a change in white and gray matter volume in brain regions of Hammers atlas in the autism group. In addition, the results of surface-based features indicate an increase in mean and standard deviation of cerebral cortex thickness and changes in cerebral cortex thickness, sulcus depth, surface complexity and gyrification index in the brain regions of the Desikan–Killany cortical atlas. Conclusions Identifying structurally abnormal areas of the brain and examining their relationship to the clinical features of Autism Spectrum Disorder can pave the way for the correct and early detection of this disorder using structural magnetic resonance imaging. It is also possible to design treatment for autistic people based on the abnormal areas of the brain, and to see the effectiveness of the treatment using imaging.

Keypoints 1. In this article, A review of the results of articles on the use of QSM technique to identify nuclei exposed to iron deposition in various neurodegenerative diseases was performed. 2. Brain nuclei with the highest changes in iron deposition were identified as a biomarker for the identification of specific neurological diseases 3. By studying recent articles, The best toolbox for each step of the QSM processing algorithm was introduced.

Precise detection of Alzheimer's disease (AD), especially at the early stages, i.e., early mild cognitive impairment (EMCI) and MCI, allows the physicians to promptly intervene to prevent the progression to advanced stages. However, identification of such stages using non-invasive brain imaging techniques like DWI, remains one of the most challenging tasks due to the subtle and mild changes in the brain structures of the subjects. Findings from previous studies suggested that topological organization alterations occur in the DTI-derived structural connectomes in MCI patients. Therefore, for improving diagnosis performance, we presented a connectome-based deep learning architecture based on BrainNet Convolutional neural network (CNN) model. The proposed model automatically extracts hidden topological features from structural networks using specially-designed convolutional filters. Experiments on 360 subjects, including 120 subjects with EMCI, 120 subjects with MCI and, 120 normal controls (NCs), with both T1-weighted MRI and DWI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI), provided the highest binary classification accuracies of 0.96, 0.98, and 0.95 for NC/EMCI, NC/MCI and EMCI/MCI respectively.In addition, we also investigated the effect of different atlas sizes and fiber descriptors as edge weights on the discriminative ability of the classification performance. Experimental results indicate that our approach exhibited superior performance to previous methods and performed effectively without any prior complex feature engineering and regardless the variability of imaging acquisition protocols and medical scanners.Finally, we observed that DTI-based graph representation of brain regions connections preserve important but hidden connectivity pattern information to discriminate between clinical profiles, and our proposed approach could be easily extended to other neurodegenerative and neuropsychiatric diseases.

Background and objectivesThe study of brain functional connectivity alterations in children with Attention-Deficit/Hyperactivity Disorder (ADHD) has been the subject of considerable investigation, but the biological mechanisms underlying these changes remain poorly understood. Here, we aim to investigate the brain alterations in patients with ADHD and Typical Development (TD) children and accurately classify ADHD children from TD controls using the graph-theoretical measures obtained from resting-state fMRI (rs-fMRI).Materials and methodsWe investigated the performances of rs-fMRI data for classifying drug-naive children with ADHD from TD controls. Fifty six drug-naive ADHD children (average age 11.86 ± 2.21 years; 49 male) and 56 age matched TD controls (average age 11.51 ± 1.77 years, 44 male) were included in this study. The graph measures extracted from rs-fMRI functional connectivity were used as features. Extracted network-based features were fed to the RFE feature selection algorithm to select the most discriminating subset of features. We trained and tested Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB) using Peking center data from ADHD-200 database to classify ADHD and TD children using discriminative features. In addition to the machine learning approach, the statistical analysis was conducted on graph measures to discover the differences in the brain network of patients with ADHD.ResultsAn accuracy of 78.2% was achieved for classifying drug-naive children with ADHD from TD controls employing the optimal features and the GB classifier. We also performed a hub node analysis and found that the number of hubs in TD controls and ADHD children were 8 and 5, respectively, indicating that children with ADHD have disturbance of critical communication regions in their brain network. The findings of this study provide insight into the neurophysiological mechanisms underlying ADHD.ConclusionPattern recognition and graph measures of the brain networks, based on the rs-fMRI data, can efficiently assist in the classification of ADHD children from TD controls.

Background and ObjectivesFocal cortical dysplasia (FCD) is a type of malformations of cortical development and one of the leading causes of drug-resistant epilepsy. Postoperative results improve the diagnosis of lesions on structural MRIs. Advances in quantitative algorithms have increased the identification of FCD lesions. However, due to significant differences in size, shape, and location of the lesion in different patients and a big deal of time for the objective diagnosis of lesion as well as the dependence of individual interpretation, sensitive approaches are required to address the challenge of lesion diagnosis. In this research, a FCD computer-aided diagnostic system to improve existing methods is presented.MethodsMagnetic resonance imaging (MRI) data were collected from 58 participants (30 with histologically confirmed FCD type II and 28 without a record of any neurological prognosis). Morphological and intensity-based features were calculated for each cortical surface and inserted into an artificial neural network. Statistical examinations evaluated classifier efficiency.ResultsNeural network evaluation metrics—sensitivity, specificity, and accuracy—were 96.7, 100, and 98.6%, respectively. Furthermore, the accuracy of the classifier for the detection of the lobe and hemisphere of the brain, where the FCD lesion is located, was 84.2 and 77.3%, respectively.ConclusionAnalyzing surface-based features by automated machine learning can give a quantitative and objective diagnosis of FCD lesions in presurgical assessment and improve postsurgical outcomes.