Rapid eye movement sleep behavior disorder and its relation to Parkinson's disease: The potential of graph measures as brain biomarkers to identify the underlying physiopathology of the disorder.

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.

Volume-based and Surface-Based Methods in Autism Compared with Healthy Controls Are Free surfer and CAT12 in Agreement?

Objectives: Autism Spectrum Disorder (ASD) encompasses a range of neurodevelopmental disorders, and early detection is crucial. This study aims to identify the Regions of Interest (ROIs) with significant differences between healthy controls and individuals with autism, as well as evaluate the agreement between FreeSurfer 6 (FS6) and Computational Anatomy Toolbox (CAT12) methods. Materials & Methods: Surface-based and volume-based features were extracted from FS software and CAT12 toolbox for Statistical Parametric Mapping (SPM) software to estimate ROI-wise biomarkers. These biomarkers were compared between 18 males Typically Developing Controls (TDCs) and 40 male subjects with ASD to assess group differences for each method. Finally, agreement and regression analyses were performed between the two methods for TDCs and ASD groups. Results: Both methods revealed ROIs with significant differences for each parameter. The Analysis of Covariance (ANCOVA) showed that both TDCs and ASD groups indicated a significant relationship between the two methods (p<0.001). The R2 values for TDCs and ASD groups were 0.692 and 0.680, respectively, demonstrating a moderate correlation between CAT12 and FS6. Bland-Altman graphs showed a moderate level of agreement between the two methods. Conclusion: The moderate correlation and agreement between CAT12 and FS6 suggest that while some consistency is observed in the results, CAT12 is not a superior substitute for FS6 software. Further research is needed to identify a potential replacement for this method.

The effect of considering eye movement time in evaluating the efficiency of attentional networks.

The attention network test (ANT) is a tool for assessing the executive, alerting, and orienting components of attention. However, conflicting findings exist regarding the nature and correlation between attention networks. This study aims to investigate the influence of eye movement time on the assessment of attention network efficiency. Forty male students, with an average age of 20.8 ± 1.3 years, participated in the study. The revised attention network test was conducted concurrently with the recording of the electrooculogram signal. The electrooculogram signal was used to estimate eye placement time on target stimuli. Considering eye movement time for calculating the score of each network was proposed as a novel method. The study explored the nature of attention networks and their relationships, and revealed significant effects for attention networks with and without considering the eye movement time. Additionally, a significant correlation is observed between the alerting and orienting networks. However, no significant correlation is found between attention networks using the proposed method. Considering eye movement time alters the assessment of attention network efficiency and modifies the correlation among attention networks.

The effect and efficiency of attentional networks and the brain electrophysiology.

The attention networks test (ANT) is frequently utilized to evaluate executive, alerting, and orienting attentional components. Additionally, it serves as an activation task in neuroimaging studies. This study aimed to examine the relationship between attention networks and brain electrophysiology. The study enrolled 40 right-handed male students (age = 20.8 ± 1.3 years) who underwent the revised attention network test, while their electroencephalogram signals were recorded. The study aimed to explore the effects of attention networks and their efficiencies on brain electrophysiology. The results indicated that the P3 amplitude was modulated by the conflict effect in the central (p-value = 0.014) and parietal (p-value = 0.002) regions. The orienting component significantly influenced P1 and N1 latencies in the parietal and parieto-occipital regions (p-values < 0.006), as well as P1 and N1 amplitude in the parieto-occipital region (p-values = 0.017 and 0.011). The alerting component significantly affected P1 latency and amplitude in the parietal and parieto-occipital regions, respectively (p-value = 0.02). Furthermore, N1 amplitude and the time interval between P1 and N1 were significantly correlated with the efficiency of alerting and orienting networks. In terms of connectivity, the coherence of theta and alpha bands significantly decreased in the incongruent condition compared to the congruent condition. Additionally, the effects of attention networks on event-related spectral perturbation were observed. The study revealed the influence of attention networks on various aspects of brain electrophysiology. Specifically, the alerting score correlated with the amplitude of the N1 component in the double-cue and no-cue conditions in the parieto-occipital region, while the orienting score in the same region correlated with the N1 amplitude in the valid cue condition and the difference in N1 amplitude between the valid cue and double-cue conditions. Overall, empirical evidence suggests that attention networks not only impact the amplitudes of electrophysiological activities but also influence their time course.

Impaired white matter integrity in infants and young children with autism spectrum disorder: What evidence does diffusion tensor imaging provide?

BACKGROUND: Abnormal functional connections are associated with impaired white matter tract integrity in the brain. Diffusion tensor imaging (DTI) is a promising method for evaluating white matter integrity in infants and young children. This work aims to shed light on the location and nature of the decrease in white matter integrity. METHODS: Here, the results of 19 studies have been presented that investigated white matter integrity in infants and young children (6 months to 12 years) with autism using diffusion tensor imaging. RESULTS: In most of the reviewed studies, an increase in Fractional Anisotropy (FA) and a decrease in Radial Diffusivity (RD) were reported in Corpus Callosum (CC), Uncinate Fasciculus (UF), Cingulum (Cg), Inferior Longitudinal Fasciculus (ILF), and Superior Longitudinal Fasciculus (SLF), and in the Inferior Fronto-Occipital Fasciculus (IFOF) tract, a decrease in FA and an increase in RD were reported. CONCLUSION: In the reviewed articles, except for one study, the diffusion indices were different compared to the control group.

Altered resting-state functional connectivity of the brain in children with autism spectrum disorder.

Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders. Brain mapping has shown that functional brain connections are altered in autism. This study investigated the pattern of brain connection changes in autistic people compared to healthy people. This study aimed to analyze functional abnormalities within the brain due to ASD, using resting-state functional magnetic resonance imaging (fMRI). Resting-state functional magnetic resonance images of 26 individuals with ASD and 26 healthy controls were obtained from the Autism Brain Imaging Data Exchange (ABIDE) database. The DPARSF (data processing assistant for resting-state fMRI) toolbox was used for resting-state functional image processing, and features related to functional connections were extracted from these images. Then, the extracted features from both groups were compared using an Independent Two-Sample T Test, and the features with significant differences between the two groups were identified. Compared with healthy controls, individuals with ASD showed hyper-connectivity in the frontal lobe, anterior cingulum, parahippocampal, left precuneus, angular, caudate, superior temporal, and left pallidum, as well as hypo-connectivity in the precentral, left superior frontal, left middle orbitofrontal, right amygdala, and left posterior cingulum. Our findings show that abnormal functional connectivity exists in patients with ASD. This study makes an important advancement in our understanding of the abnormal neurocircuits causing autism.

Classification of drug-naive children with attention-deficit/hyperactivity disorder from typical development controls using resting-state fMRI and graph theoretical approach.

Background and objectives: The 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 methods: We 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. Results: An 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. Conclusion: Pattern 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.

Comparison of machine learning methods for the detection of focal cortical dysplasia lesions: decision tree, support vector machine and artificial neural network.

BACKGROUND: Accurate classification of focal cortical dysplasia (FCD) has been challenging due to the problematic visual detection in magnetic resonance imaging (MRI). Hence, recently, there has been a necessity for employing new techniques to solve the problem. Among the new techniques for FCD lesion diagnosis, classification techniques can be of great help in FCD patient's detection from healthy individuals. METHODS: MRI data were collected from 58 participants (30 subjects with FCD type II and 28 normal subjects). Morphological and intensity-based characteristics were calculated for each cortical level and then the performance of the three classifiers: decision tree (DT), support vector machine (SVM) and artificial neural network (ANN) was evaluated. RESULTS: Metrics for evaluating classification methods, sensitivity, specificity and accuracy for the DT were 90%, 100% and 95.8%, respectively; it was 95%, 100% and 97.9% for the SVM and 96.7%, 100% and 98.6% for the ANN. CONCLUSION: Comparison of the performance of the three classifications used in this study showed that all three have excellent performance in specificity, but in terms of classification sensitivity and accuracy, the artificial neural network method has worked better.

Early differentiation of neurodegenerative diseases using the novel QSM technique: what is the biomarker of each disorder?

During neurodegenerative diseases, the brain undergoes morphological and pathological changes; Iron deposits are one of the causes of pathological changes in the brain. The Quantitative susceptibility mapping (QSM) technique, a type of magnetic resonance (MR) image reconstruction, is one of the newest diagnostic methods for iron deposits to detect changes in magnetic susceptibility. Numerous research projects have been conducted in this field. The purpose of writing this review article is to identify the first deep brain nuclei that undergo magnetic susceptibility changes during neurodegenerative diseases such as Alzheimer's or Parkinson's disease. The purpose of this article is to identify the brain nuclei that are prone to iron deposition in any specific disorder. In addition to the mentioned purpose, this paper proposes the optimal scan parameters and appropriate algorithms of each QSM reconstruction step by reviewing the results of different articles. As a result, The QSM technique can identify nuclei exposed to iron deposition in various neurodegenerative diseases. Also, the selection of scan parameters is different based on the sequence and purpose; an example of the parameters is placed in the tables. The BET toolbox in FSL, Laplacian-based phase-unwrapping process, the V_SHARP algorithm, and morphology-enabled dipole inversion (MEDI) method are the most widely used algorithms in various stages of QSM reconstruction.

Brain pathological changes during neurodegenerative diseases and their identification methods: How does QSM perform in detecting this process?

The presence of iron is essential for many biological processes in the body. But sometimes, for various reasons, the amount of iron deposition in different areas of the brain increases, which leads to problems related to the nervous system. Quantitative susceptibility mapping (QSM) is one of the newest magnetic resonance imaging (MRI)-based methods for assessing iron accumulation in target areas. This Narrative Review article aims to evaluate the performance of QSM compared to other methods of assessing iron deposition in the clinical field. Based on the results, we introduced related basic definitions, some neurodegenerative diseases, methods of examining iron deposition in these diseases, and their advantages and disadvantages. This article states that the QSM method can be introduced as a new, reliable, and non-invasive technique for clinical evaluations.

Features of the superficial white matter as biomarkers for the detection of Alzheimer's disease and mild cognitive impairment: A diffusion tensor imaging study.

BACKGROUND: With the development of medical imaging and processing tools, accurate diagnosis of diseases has been made possible by intelligent systems. Owing to the remarkable ability of support vector machines (SVMs) for diseases diagnosis, extensive research has been conducted using the SVM algorithm for the classification of Alzheimer's disease (AD) and mild cognitive impairment (MCI). OBJECTIVES: In this study, we applied an automated method to classify patients with AD and MCI and healthy control (HC) subjects based on the diffusion tensor imaging (DTI) features in the superficial white matter (SWM). PARTICIPANTS: For this purpose, DTI data were downloaded from the Alzheimer's Disease Neuroimaging Initiative (ADNI). This method employed DTI data from 72 subjects: 24 subjects as HC, 24 subjects with MCI, and 24 subjects with AD. MEASURE: ments: DTI processing was performed using DSI Studio software and all machine learning analyses were performed using MATLAB software. RESULTS: The linear kernel of SVM was the best classifier, with an accuracy of 95.8% between the AD and HC groups, followed by the quadratic kernel of SVM with an accuracy of 83.3% between the MCI and HC groups and the Gaussian kernel of SVM with an accuracy of 83.3% between the AD and MCI groups. CONCLUSIONS: Given the importance of diagnosing AD and MCI as well as the role of superficial white matter in the diagnosis of neurodegenerative diseases, in this study, the features of different DTI methods of the SWM are discussed, which could be a useful tool to assist in the diagnosis of AD and MCI.

Automatic Detection of Focal Cortical Dysplasia Type II in MRI: Is the Application of Surface-Based Morphometry and Machine Learning Promising?

BACKGROUND AND OBJECTIVES: Focal 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. METHODS: Magnetic 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. RESULTS: Neural 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. CONCLUSION: Analyzing surface-based features by automated machine learning can give a quantitative and objective diagnosis of FCD lesions in presurgical assessment and improve postsurgical outcomes.

Identification of Superficial White Matter Abnormalities in Alzheimer's Disease and Mild Cognitive Impairment Using Diffusion Tensor Imaging.

BACKGROUND: Diffusion tensor imaging (DTI) estimates the microstructural alterations of the brain, as a magnetic resonance imaging (MRI)-based neuroimaging technique. Prior DTI studies reported decreased structural integrity of the superficial white matter (SWM) in the brain diseases. OBJECTIVE: This study aimed to determine the diffusion characteristics of SWM in Alzheimer's disease (AD) and mild cognitive impairment (MCI) using tractography and region of interest (ROI) approaches. METHODS: The diffusion MRI data were downloaded from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database on 24 patients with AD, 24 with MCI, and 24 normal control (NC) subjects. DTI processing was performed using DSI Studio software. First, for ROI-based analysis, The superficial white matter was divided into right and left frontal, parietal, temporal, insula, limbic and occipital regions by the Talairach Atlas, Then, for tractography-based analysis, the tractography of each of these regions was performed with 100000 seeds. Finally, the average diffusion values were extracted from voxels within the ROIs and tracts. RESULTS: Both tractography and ROI analyses showed a significant difference in radial, axial and mean diffusivity values between the three groups (p < 0.05) across most of the SWM. Furthermore, The Mini-Mental State Examination was significantly correlated with radial, axial, and mean diffusivity values in parietal and temporal lobes SWM in the AD group (p < 0.05). CONCLUSION: DTI provided information indicating microstructural changes in the SWM of patients with AD and MCI. Therefore, assessment of the SWM using DTI may be helpful for the clinical diagnosis of patients with AD and MCI.

An effective hair removal algorithm for dermoscopy images.

BACKGROUND/PURPOSE: Dermoscopy is one of the major imaging modalities used in the diagnosis of pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, computerized image analysis techniques have become important tools in this research area. Hair removal from skin lesion images is one of the key problems for the precise segmentation and analysis of the skin lesions. In this study, we present a new scheme that automatically detects and removes hairs from dermoscopy images. METHODS: The proposed algorithm includes two steps: firstly, light and dark hairs and ruler marking are segmented through adaptive canny edge detector and refinement by morphological operators. Secondly, the hairs are repaired based on multi-resolution coherence transport inpainting. RESULTS: The algorithm was applied to 50 dermoscopy images. To estimate the accuracy of the proposed hair detection algorithm, quantitative analysis was performed using TDR, FPR, and DA metrics. Moreover, to evaluate the performance of the proposed hair repaired algorithm, three statistical metrics namely entropy, standard deviation, and co-occurrence matrix were used. CONCLUSION: The results demonstrate that the proposed algorithm is highly accurate and able to detect and repair the hair pixels with few errors. In addition, the segmentation veracity of the skin lesion is effectively improved after our proposed hair removal algorithm.