DeepRetroMoCo: deep neural network-based retrospective motion correction algorithm for spinal cord functional MRI.

Background and purpose: There are distinct challenges in the preprocessing of spinal cord fMRI data, particularly concerning the mitigation of voluntary or involuntary movement artifacts during image acquisition. Despite the notable progress in data processing techniques for movement detection and correction, applying motion correction algorithms developed for the brain cortex to the brainstem and spinal cord remains a challenging endeavor. Methods: In this study, we employed a deep learning-based convolutional neural network (CNN) named DeepRetroMoCo, trained using an unsupervised learning algorithm. Our goal was to detect and rectify motion artifacts in axial T2*-weighted spinal cord data. The training dataset consisted of spinal cord fMRI data from 27 participants, comprising 135 runs for training and 81 runs for testing. Results: To evaluate the efficacy of DeepRetroMoCo, we compared its performance against the sct_fmri_moco method implemented in the spinal cord toolbox. We assessed the motion-corrected images using two metrics: the average temporal signal-to-noise ratio (tSNR) and Delta Variation Signal (DVARS) for both raw and motion-corrected data. Notably, the average tSNR in the cervical cord was significantly higher when DeepRetroMoCo was utilized for motion correction, compared to the sct_fmri_moco method. Additionally, the average DVARS values were lower in images corrected by DeepRetroMoCo, indicating a superior reduction in motion artifacts. Moreover, DeepRetroMoCo exhibited a significantly shorter processing time compared to sct_fmri_moco. Conclusion: Our findings strongly support the notion that DeepRetroMoCo represents a substantial improvement in motion correction procedures for fMRI data acquired from the cervical spinal cord. This novel deep learning-based approach showcases enhanced performance, offering a promising solution to address the challenges posed by motion artifacts in spinal cord fMRI data.

Intranasal delivery of sunitinib: A new therapeutic approach for targeting angiogenesis of glioblastoma.

Glioblastoma multiforme (GBM) is one of the most vascular among solid tumors, and despite the use of multimodal therapies, the survival of these patients is poor. In order to target angiogenesis in GBM as a promising strategy, an antiangiogenic drug is required. This study was designed to evaluate the effects of sunitinib, a multityrosine kinase inhibitor with tumor proliferation and angiogenesis inhibitory properties, on GBM-bearing rats. Given the ineffective drug delivery to the brain due to the presence of the blood-brain barrier (BBB), intra-nasal (IN) drug delivery has recently been considered as a non-invasive method to bypass BBB. Therefore, in the current study, IN was used as an ideal method for the delivery of sunitinib to the brain, and the effects of this method were also compared to the OR administration of the sunitinib. GBM was induced in the brain of male Wistar rats, and they were randomly divided into 4 groups; IN-STB (sunitinib intranasal delivery), IN-sham (placebo intranasal delivery), OR-STB (sunitinib oral delivery) and OR-sham (placebo oral delivery). After the end of the treatment period, an MRI of animals' brains showed a reduction in tumor growth in the treatment groups. Immunohistochemistry revealed that sunitinib inhibits angiogenesis in GBM in both OR and IN delivery methods. Analysis of liver tissue and enzymes showed that IN delivery of sunitinib had less hepatotoxicity than the OR method. Overall, it was found that IN sunitinib delivery could be used as a potential non-hepatotoxic alternative for the treatment of GBM.

Iranian Brain Imaging Database: A Neuropsychiatric Database of Healthy Brain.

INTRODUCTION: The Iranian Brain Imaging Database (IBID) was initiated in 2017, with 5 major goals: provide researchers easy access to a neuroimaging database, provide normative quantitative measures of the brain for clinical research purposes, study the aging profile of the brain, examine the association of brain structure and function, and join the ENIGMA consortium. Many prestigious databases with similar goals are available. However, they were not done on an Iranian population, and the battery of their tests (e.g. cognitive tests) is selected based on their specific questions and needs. METHODS: The IBID will include 300 participants (50% female) in the age range of 20 to 70 years old, with an equal number of participants (#60) in each age decade. It comprises a battery of cognitive, lifestyle, medical, and mental health tests, in addition to several Magnetic Resonance Imaging (MRI) protocols. Each participant completes the assessments on two referral days. RESULTS: The study currently has a cross-sectional design, but longitudinal assessments are considered for the future phases of the study. Here, details of the methodology and the initial results of assessing the first 152 participants of the study are provided. CONCLUSION: IBID is established to enable research into human brain function, to aid clinicians in disease diagnosis research, and also to unite the Iranian researchers with interests in the brain.

A Combination of Particle Swarm Optimization and Minkowski Weighted K-Means Clustering: Application in Lateralization of Temporal Lobe Epilepsy.

K-Means is one of the most popular clustering algorithms that partitions observations into nonoverlapping subgroups based on a predefined similarity metric. Its drawbacks include a sensitivity to noisy features and a dependency of its resulting clusters upon the initial selection of cluster centroids resulting in the algorithm converging to local optima. Minkowski weighted K-Means (MWK-Means) addresses the issue of sensitivity to noisy features, but is sensitive to the initialization of clusters, and so the algorithm may similarly converge to local optima. Particle Swarm Optimization (PSO) uses a globalized search method to solve this issue. We present a hybrid Particle Swarm Optimization (PSO) + MWK-Means clustering algorithm to address all the above problems in a single framework, while maintaining benefits of PSO and MWK Means methods. This study investigated the utility of this approach in lateralizing the epileptogenic hemisphere for temporal lobe epilepsy (TLE) cases using magnetoencephalography (MEG) coherence source imaging (CSI) and diffusion tensor imaging (DTI). Using MEG-CSI, we analyzed preoperative resting state MEG data from 17 adults TLE patients with Engel class I outcomes to determine coherence at 54 anatomical sites and compared the results with 17 age- and gender-matched controls. Fiber-tracking was performed through the same anatomical sites using DTI data. Indices of both MEG coherence and DTI nodal degree were calculated. A PSO + MWK-Means clustering algorithm was applied to identify the side of temporal lobe epileptogenicity and distinguish between normal and TLE cases. The PSO module was aimed at identifying initial cluster centroids and assigning initial feature weights to cluster centroids and, hence, transferring to the MWK-Means module for the final optimal clustering solution. We demonstrated improvements with the use of the PSO + MWK-Means clustering algorithm compared to that of K-Means and MWK-Means independently. PSO + MWK-Means was able to successfully distinguish between normal and TLE in 97.2% and 82.3% of cases for DTI and MEG data, respectively. It also lateralized left and right TLE in 82.3% and 93.6% of cases for DTI and MEG data, respectively. The proposed optimization and clustering methodology for MEG and DTI features, as they relate to focal epileptogenicity, would enhance the identification of the TLE laterality in cases of unilateral epileptogenicity.

Fully automatic 3D segmentation of the thoracolumbar spinal cord and the vertebral canal from T2-weighted MRI using K-means clustering algorithm.

STUDY DESIGN: Method development. OBJECTIVES: To develop a reliable protocol for automatic segmentation of Thoracolumbar spinal cord using MRI based on K-means clustering algorithm in 3D images. SETTING: University-based laboratory, Tehran, Iran. METHODS: T2 structural volumes acquired from the spinal cord of 20 uninjured volunteers on a 3T MR scanner. We proposed an automatic method for spinal cord segmentation based on the K-means clustering algorithm in 3D images and compare our results with two available segmentation methods (PropSeg, DeepSeg) implemented in the Spinal Cord Toolbox. Dice and Hausdorff were used to compare the results of our method (K-Seg) with the manual segmentation, PropSeg, and DeepSeg. RESULTS: The accuracy of our automatic segmentation method for T2-weighted images was significantly better or similar to the SCT methods, in terms of 3D DC (p < 0.001). The 3D DCs were respectively (0.81 ± 0.04) and Hausdorff Distance (12.3 ± 2.48) by the K-Seg method in contrary to other SCT methods for T2-weighted images. CONCLUSIONS: The output with similar protocols showed that K-Seg results match the manual segmentation better than the other methods especially on the thoracolumbar levels in the spinal cord due to the low image contrast as a result of poor SNR in these areas.

Effect of Physiological Noise on Thoracolumbar Spinal Cord Functional Magnetic Resonance Imaging in 3T Magnetic Field.

INTRODUCTION: Functional Magnetic Resonance Imaging (fMRI) methods have been used to study sensorimotor processing in the spinal cord. However, these techniques confront unwanted noises to the measured signal from the physiological fluctuations. In the spinal cord imaging, most of the challenges are consequences of cardiac and respiratory movement artifacts that are considered as significant sources of noise, especially in the thoracolumbar region. In this study, we investigated the effect of each source of physiological noise and their contribution to the outcome of the analysis of the blood-oxygen-level-dependent signal in the human thoracolumbar spinal cord. METHODS: Fifteen young healthy male volunteers participated in the study, and pain stimuli were delivered on the L5 dermatome between the two malleoli. Respiratory and cardiac signals were recorded during the imaging session, and the generated respiration and cardiac regressors were included in the general linear model for quantification of the effect of each of them on the task-analysis results. The sum of active voxels of the clusters was calculated in the spinal cord in three correction states (respiration correction only, cardiac correction only, and respiration and cardiac noise corrections) and analyzed with analysis of variance statistical test and receiver operating characteristic curve. RESULTS: The results illustrated that cardiac noise correction had an effective role in increasing the active voxels (Mean±SD = 23.46±9.46) compared to other noise correction methods. Cardiac effects were higher than other physiological noise sources. CONCLUSION: In summary, our results indicate great respiration effects on the lumbar and thoracolumbar spinal cord fMRI, and its contribution to the heartbeat effect can be a significant variable in the individual fMRI data analysis. Displacement of the spinal cord and the effects of this noise in the thoracolumbar and lumbar spinal cord fMRI results are significant and cannot be ignored.

Phylogeny of Symbiodinium populations in zoantharians of the northern Persian Gulf.

Zoantharians of the Persian Gulf (PG) experience periods of anomalous high temperature, irradiance and desiccation. Their survival largely relies on the symbiotic relationship with single celled dinoflagellates of the genus Symbiodinium. However, the phylogeny of symbionts of zoantharians has not been investigated in the region. In this study, the second internal transcribed spacer region of ribosomal DNA (ITS2) was used to recognize in hospite populations of Symbiodinium in Palythoa aff. mutuki, Palythoa tuberculosa and Zoanthus sansibaricus colonies from Hengam, Kish, Larak, and Qeshm Islands, in the PG. The results showed subclade D1-4 and a variant of A1, were the most prevalent subclades of Symbiodinium. Predominance of stress tolerant subclade D1-4 and putatively radiation tolerant variant of A1 of Symbiodinium in zoantharian species might suggest an adaptation strategy to the extreme physical environment of the PG.