White Matter
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2021 ◽  
Vol 118 ◽  
pp. 102038
Zahra Namvarpour ◽  
Kobra Afsordeh ◽  
Abdollah Amini ◽  
Fatemeh Fadaei Fathabady

Fumika Azuma ◽  
Kazuya Nokura ◽  
Tetsuharu Kako ◽  
Mari Yoshida ◽  
Shinsui Tatsumi

Lupus ◽  
2021 ◽  
pp. 096120332110450
Cong Zhou ◽  
Man Dong ◽  
Weiwei Duan ◽  
Hao Lin ◽  
Shuting Wang ◽  

Background Systemic lupus erythematosus is often accompanied with neuropsychiatric symptoms. Neuroimaging evidence indicated that microstructural white matter (WM) abnormalities play role in the neuropathological mechanism. Diffusion tensor imaging (DTI) studies allows the assessment of the microstructural integrity of WM tracts, but existing findings were inconsistent. This present study aimed to conduct a coordinate‐based meta‐analysis (CBMA) to identify statistical consensus of DTI studies in SLE. Methods Relevant studies that reported the differences of fractional anisotropy (FA) between SLE patients and healthy controls (HC) were searched systematically. Only studies reported the results in Talairach or Montreal Neurological Institute (MNI) coordinates were included. The anisotropic effect size version of signed differential mapping (AES-SDM) was applied to detect WM alterations in SLE. Results Totally, five studies with seven datasets which included 126 patients and 161 HC were identified. The pooled meta-analysis demonstrated that SLE patients exhibited significant FA reduction in the left striatum and bilateral inferior network, mainly comprised the corpus callosum (CC), bilateral inferior fronto-occipital fasciculus (IFOF), bilateral anterior thalamic projections, bilateral superior longitudinal fasciculus (SLF), left inferior longitudinal fasciculus (ILF), and left insula. No region with higher FA was identified. Conclusions Disorders of the immune system might lead to subtle WM microstructural alterations in SLE, which might be related with cognitive deficits or emotional distress symptoms. This provides a better understanding of the pathological mechanism of microstructural brain abnormalities in SLE.

2021 ◽  
Vol 9 (1) ◽  
Gisela Nilsson ◽  
Ana A. Baburamani ◽  
Mary A. Rutherford ◽  
Changlian Zhu ◽  
Carina Mallard ◽  

AbstractOsteopontin (OPN) is a matricellular protein that mediates various physiological functions and is implicated in neuroinflammation, myelination, and perinatal brain injury. However, its expression in association with brain injury in preterm infants is unexplored. Here we examined the expression of OPN in postmortem brains of preterm infants and explored how this expression is affected in brain injury. We analyzed brain sections from cases with white matter injury (WMI) and cases with germinal matrix hemorrhage (GMH) and compared them to control cases having no brain injury. WMI cases displayed moderate to severe tissue injury in the periventricular and deep white matter that was accompanied by an increase of microglia with amoeboid morphology. Apart from visible hemorrhage in the germinal matrix, GMH cases displayed diffuse white matter injury in the periventricular and deep white matter. In non-injured preterm brains, OPN was expressed at low levels in microglia, astrocytes, and oligodendrocytes. OPN expression was significantly increased in regions with white matter injury in both WMI cases and GMH cases. The main cellular source of OPN in white matter injury areas was amoeboid microglia, although a significant increase was also observed in astrocytes in WMI cases. OPN was not expressed in the germinal matrix of any case, regardless of whether there was hemorrhage. In conclusion, preterm brain injury induces elevated OPN expression in microglia and astrocytes, and this increase is found in sites closely related to injury in the white matter regions but not with the hemorrhage site in the germinal matrix. Thus, it appears that OPN takes part in the inflammatory process in white matter injury in preterm infants, and these findings facilitate our understanding of OPN’s role under both physiological and pathological conditions in the human brain that may lead to greater elucidation of disease mechanisms and potentially better treatment strategies.

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258480
Jae Hun Oh ◽  
Seung Pill Choi ◽  
Jong Ho Zhu ◽  
Soo Hyun Kim ◽  
Kyu Nam Park ◽  

The gray-to-white matter ratio (GWR) has been used to identify brain damage in comatose patients after cardiac arrest. However, Hounsfield units (HUs), the measurement of brain density on computed tomography (CT) images, may vary depending on the machine type or parameter. Therefore, differences in CT scanners may affect the GWR in post-cardiac arrest patients. We performed a retrospective study on comatose post-cardiac arrest patients who visited the hospital from 2007 to 2017. Two CT, Lightspeed and SOMATOM, scanners were used. Two observers independently measured the HUs of the caudate nucleus, putamen, posterior internal capsule, and corpus callosum using regions of interest. We compared the GWR calculated from the HUs measured at different CT scanners. The analysis of different scanners showed statistically significant differences in the measured HUs and GWR. The HUs and GWR of Lightspeed were measured lower than SOMATOM. The difference between the two CT scanners was also evident in groups divided by neurological prognosis. The area under the curve of the receiver operating characteristic curve to predict poor outcomes of Lightspeed was 0.798, and the cut-off value for 100% specificity was 1.172. The SOMATOM was 0.855, and the cut-off value was 1.269. The difference in scanners affects measurements and performance characteristics of the GWR in post-cardiac arrest patients. Therefore, when applying the results of the GWR study to clinical practice, reference values for each device should be presented, and an integrated plan should be prepared.

2021 ◽  
Bo-Gyeom Kim ◽  
Gun Ahn ◽  
Sooyoung Kim ◽  
Kakyeong Kim ◽  
Hyeonjin Kim ◽  

Suicide is among the leading causes of death in youth worldwide. Early identification of children with high risk for suicide is key to effective screening and prevention strategies. Brain imaging can show functional or structural abnormalities related to youth suicidality, but literature is scarce. Here we tested the extent to which brain imaging is useful in predicting suicidal risk in children. In the largest to date, multi-site, multi-ethnic, epidemiological developmental samples in the US (N = 6,172; the ABCD study), we trained and validated machine learning models and deep neural networks on the multimodal brain imaging derived phenotypes (morphometry, white matter connectivity, functional activation, and connectivity) along with behavioral and self-reported psychological questionnaire data. The model trained on diffusion white matter connectomes showed the best performance (test AUC-ROC = 74.82) with a one percentage increase compared with the baseline model trained on behavioral and psychological data (test AUC-ROC = 74.16). Models trained on other MRI modalities showed similar but slightly lower performances. Model interpretation showed the important brain features involved in attention, emotion regulation, and motor coordination, such as the anterior cingulate cortex, temporal gyrus, and precentral gyrus. It further showed that the interaction of brain features with depression and impulsivity measures contributed to the optimal prediction of youth suicidality. This study demonstrates the potential utility of a multimodal brain imaging approach to youth suicidality prediction and uncovers the relationships of the psychological and multi-dimensional and multi-modal neural features to youth suicidality.

Josue M. Avecillas-Chasin ◽  
Joohi Jimenez-Shahed ◽  
Joan Miravite ◽  
Susan Bressman ◽  
Brian H Kopell

We present a patient with severe life-threatening dyskinesias due to a persistent microlesion effect after STN-DBS electrode implantation. The pallidofugal pathways were identified using patient-specific tractography, and steering the current toward this white matter structure resulted in complete resolution of the severe dyskinesias.

Cancers ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 5157
Vianney Gilard ◽  
Justine Ferey ◽  
Florent Marguet ◽  
Maxime Fontanilles ◽  
Franklin Ducatez ◽  

(1) Background: Glioblastoma is the most common malignant brain tumor in adults. Its etiology remains unknown in most cases. Glioblastoma pathogenesis consists of a progressive infiltration of the white matter by tumoral cells leading to progressive neurological deficit, epilepsy, and/or intracranial hypertension. The mean survival is between 15 to 17 months. Given this aggressive prognosis, there is an urgent need for a better understanding of the underlying mechanisms of glioblastoma to unveil new diagnostic strategies and therapeutic targets through a deeper understanding of its biology. (2) Methods: To systematically address this issue, we performed targeted and untargeted metabolomics-based investigations on both tissue and plasma samples from patients with glioblastoma. (3) Results: This study revealed 176 differentially expressed lipids and metabolites, 148 in plasma and 28 in tissue samples. Main biochemical classes include phospholipids, acylcarnitines, sphingomyelins, and triacylglycerols. Functional analyses revealed deep metabolic remodeling in glioblastoma lipids and energy substrates, which unveils the major role of lipids in tumor progression by modulating its own environment. (4) Conclusions: Overall, our study demonstrates in situ and systemic metabolic rewiring in glioblastoma that could shed light on its underlying biological plasticity and progression to inform diagnosis and/or therapeutic strategies.

2021 ◽  
Ahmed M. Radwan ◽  
Stefan Sunaert ◽  
Kurt G. Schilling ◽  
Maxime Descoteaux ◽  
Bennett A. Landman ◽  

Virtual dissection of white matter (WM) using diffusion MRI tractography is confounded by its poor reproducibility. Despite the increased adoption of advanced reconstruction models, early region-of-interest driven protocols based on diffusion tensor imaging (DTI) remain the dominant reference for virtual dissection protocols. Here we bridge this gap by providing a comprehensive description of typical WM anatomy reconstructed using a reproducible automated subject-specific parcellation-based approach based on probabilistic constrained-spherical deconvolution (CSD) tractography. We complement this with a WM template in MNI space comprising 68 bundles, including all associated anatomical tract selection labels and associated automated workflows. Additionally, we demonstrate bundle inter- and intra-subject variability using 40 (20 test-retest) datasets from the human connectome project (HCP) and 5 sessions with varying b-values and number of b-shells from the single-subject Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation (MASSIVE) dataset. The most reliably reconstructed bundles were the whole pyramidal tracts, primary corticospinal tracts, whole superior longitudinal fasciculi, frontal, parietal and occipital segments of the corpus callosum and middle cerebellar peduncles. More variability was found in less dense bundles, e.g., the first segment of the superior longitudinal fasciculus, fornix, dentato-rubro-thalamic tract (DRTT), and premotor pyramidal tract. Using the DRTT as an example, we show that this variability can be reduced by using a higher number of seeding attempts. Overall inter-session similarity was high for HCP test-retest data (median weighted-dice = 0.963, stdev = 0.201 and IQR = 0.099). Compared to the HCP-template bundles there was a high level of agreement for the HCP test-retest data (median weighted-dice = 0.747, stdev = 0.220 and IQR = 0.277) and for the MASSIVE data (median weighted-dice = 0.767, stdev = 0.255 and IQR = 0.338). In summary, this WM atlas provides an overview of the capabilities and limitations of automated subject-specific probabilistic CSD tractography for mapping white matter fasciculi in healthy adults. It will be most useful in applications requiring a highly reproducible parcellation-based dissection protocol, as well as being an educational resource for applied neuroimaging and clinical professionals.

2021 ◽  
Gwang-Won Kim ◽  
Kwangsung Park ◽  
Gwang-Woo Jeong

Abstract The incidence of Alzheimer’s disease (AD) has been increasing each year; however, few methods are available to identify the effects of treatment for AD. Defective hippocampus has been associated with mild cognitive impairment (MCI), an early stage of AD. However, the effect of donepezil treatment on hippocampus-related networks is unknown. The purpose of this study was to evaluate the hippocampal white matter (WM) connectivity following donepezil treatment in patients with MCI using probabilistic tractography, and to further determine the WM integrity and changes in brain volume. Magnetic resonance imaging and diffusion tensor imaging (DTI) data of patients with MCI before and after 6-month donepezil treatment were acquired. Volumes and DTI scalars of 11 regions of interest comprising the frontal and temporal cortices and subcortical regions were measured. Seed-based structural connectivity analyses were focused on the hippocampus. Compared with healthy controls, patients with MCI showed significantly decreased hippocampal volume and WM connectivity with the superior frontal gyrus, as well as increased mean diffusivity (MD) and radial diffusivity (RD) in the amygdala (p < 0.05, Bonferroni-corrected). After six months of donepezil treatment, patients with MCI showed increased hippocampal-inferior temporal gyrus (ITG) WM connectivity (p < 0.05, Bonferroni-corrected), which was normalized to the healthy control. These findings will be useful in developing theories to describe the etiology of MCI and the therapeutic role of anticholinesterases.

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